CodeSignal launches AI-powered leadership training platform with voice simulation, expanding beyond technical assessments to democratize soft skills development at scale for middle managers and professionals.Read More

EA's Battlefield Labs test program is "the most ambitious community collaboration in franchise history"CEO Andrew Wilson said the response thus far was "well beyond expectations"Image credit: EA News by Vikki Blake Contributor Published on Feb. 5, 2025 Electronic Arts has launched Battlefield Labs, a new community test program it believes is "the most ambitious community collaboration in franchise history".With the new phase set to go live in "the coming weeks", EA is inviting players to sign up now to test the early experiences of its projects ahead of launch.Addressing investors in its Q3 earnings call, CEO Andrew Wilson said the response to the program thus far was "well beyond [EA's] expectations"."[This] is the biggest Battlefield we will ever build or at least we have ever built to date. It exists on an incredible scale, both in terms of breadth and depth of gameplay in terms of that you can play this game," Wilson said."And a big part of the modern development process that the team is taking is to test and tune everything to ensure that even as we launch something of this scale, it launches both stable and secure."And I think the combination of those two things is driving this initiative, which is Battlefield Labs, and the way the community has responded has been very positive to date," Wilson concluded.In EA's latest earnings report, the company admitted Q3 was "not the financial performance we wanted or expected".

When it comes to improving the sustainability of consumer electronics, theres a growing movement to design devices with a focus on upgradability and repairability that can extend their longevity, instead of just making them easier to recycle after a few years of use.At one time, nearly every laptop maker was caught up in a race to create ultrathin designs, resulting in hardware that was difficult to upgrade and expensive to repair. But in recent years, several companies have demonstrated that laptops can be designed so that users can upgrade and easily swap out parts as needed.Innovative companies like Framework have been producing laptops that dont need to be entirely replaced every few years, and with more success than other companies previously following similar pursuits. In 2021, Dell was sued over its Alienware laptop that promised GPU upgrades, while Intel has abandoned a couple of modular hardware products, including its Compute Cards.For now, its primarily Framework leading the charge, but its success has inspired competitors like Lenovo to follow suit. As more companies dedicate R&D to the cause, Framework may one day no longer be the only brand associated with modular devices.You can stay on top of all the latest upgradable and easily repairable device news and developments right here.HighlightsToday, 15 seconds agoAndrew LiszewskiFrameworks open source RISC-V laptop mainboard is now available.Originally announced last June, the open source RISC-V mainboard that Framework developed with DeepComputing is now available for $199. Its compatible with Frameworkss 13-inch modular laptop, as well as the $39 Cooler Master case that can turn it into a desktop PC.Its powered by a StarFive JH7110 SoC processor instead of chips from Intel or AMD, but its not recommended for most consumers. Its targeted at developers and early adopters not focused on performance.Frameworks RISC-V mainboard for its Laptop 13 is now available for $199. Image: FrameworkJan 11Sean HollisterIntel still dreams of modular PCs it brought a tablet laptop gaming handheld to CESPhoto by Sean Hollister/ The VergeAt CES 2025, Intel let journalists into its private Innovation Showcase, where we saw things like prototype next-gen laptops and giant stereo 3D handheld gaming PCs.While I was there, I also spotted a heavy metal handheld on a table that didnt seem... fully attached... to its screen. When I lifted the screen, it came away easily.Read Article >Jan 6Sean HollisterDells new USB-C ports use screws instead of solder so you can fix them yourself.Dell just killed off XPS, but its new Pro laptops pull a neat trick: most USB-C ports and batteries are now officially user-replaceable.This is the first time that weve had a screwed-on, non-soldered modular USB-C port, Dell PM Katie Green tells us. She says Dell also plans to bring this to consumers when it makes sense. No word on Framework-like modularity yet.1/3The new modular USB-C port. Images: DellDec 17, 2024Sean HollisterThe Framework Laptop 16 just got a modular gadget that enables quadruple SSDsFrameworks double SSD caddy for its Framework 16 expansion bay. Image: FrameworkThe most ambitious laptop ever made just got a long-promised modular upgrade. Starting today, you can pay $39 to add two extra M.2 slots to the Framework Laptop 16 letting you potentially carry around an AI accelerator, an eGPU adapter, or a grand total of four solid state storage sticks for ludicrous capacity.As Frameworks blog post points out, the new Dual M.2 Adapter is Frameworks first new modular component since launch that takes advantage of the Laptop 16s big expansion bay around back. At launch, you only had two options: a Radeon RX 7700S discrete graphics card for extra money, or a mostly empty bay that only contained fans.Read Article >Oct 7, 2024Andrew LiszewskiYou can now accessorize your Framework laptop with Lego.If youre a Framework laptop user with access to a 3D printer youve now got an important question to ask yourself. Do you prioritize being able to accessorize your machine with Lego using this 3D-printed adapter with studs and tubes that fits into the Frameworks expansion card port? Or is adding another USB-C or HDMI port a higher priority? Decisions, decisions...Got Lego? You can use it to further upgrade your Framework laptop with this 3D-printed adapter. Image: PrintablesJun 28, 2024Sean HollisterFramework Laptop 16, six months laterMy original Framework Laptop 16 review unit, next to its replacement.In January, I spent two weeks with the most modular notebook ever made: the Framework Laptop 16. Its a gadget nerds dream: you can swap out its keyboard, touchpad, ports even its entire discrete GPU. You can transform it from a sleek work laptop to a decent gaming machine in two minutes flat, one which charges with the worlds first 180W USB-C power adapter.But at the time, I gave the Framework Laptop 16 a 5 out of 10. The product gave me multiple Blue Screens of Death, glitched, felt flimsy in places, and ran hotter and louder than its performance would suggest. Read Article >Jun 18, 2024Sean HollisterThe Framework Laptop 13 is about to become one of the worlds first RISC-V laptopsPhoto by Amelia Holowaty Krales / The VergeWhat if your laptop didnt need a processor from an established brand like Intel or AMD? What if it didnt even rely on proprietary paid architectures like Arm and x86? RISC-V is one of the answers to those questions its free for anyone to use! and modular computer company Framework has just decided to welcome RISC-V into its laptop lineup.Specifically, Framework has partnered with DeepComputing, the company behind the very first RISC-V laptops, to build a mainboard for the Framework Laptop 13 as well. Framework already sells Intel and AMD mainboards that easily slot into its 13-inch chassis, and DeepComputing has now independently designed a new one you could drop into Frameworks laptop or even this $39 Cooler Master case that turns those boards into mini-desktops.Read Article >May 29, 2024Sean HollisterFramework now lets you upgrade its Laptop 13 with a drop-in 120Hz screen, better webcam, and Core Ultra chipImage: FrameworkI know, I absolutely stuffed this storys headline but Im just so excited to see the future of modular computing unfolding before my eyes. Framework, the company that actually delivered on the promise of an upgradable laptop, has apparently done it yet again.Not only is it now taking preorders for yet another generation of swappable mainboards to give you Intel Core Ultra chips, but the company has also developed a $39 webcam to improve the originals middling image quality and a $269 drop-in display that replaces your 60Hz, 2256 x 1504 screen with a brighter, higher-res, variable refresh rate 120Hz 2880 x 1920 panel at 256ppi that should make everything smoother and crisper.Read Article >May 7, 2024Emma RothCompression-mounted laptop RAM is fast, efficient, and upgradeableThe first laptop with LPCAMM2 memory is finally here. The folks at iFixit got their hands on the Lenovo ThinkPad P1 (Gen 7), which uses LPDDR5X memory in an LPCAMM2 module that you can upgrade or replace by simply unscrewing from the laptops motherboard.Thats a major change from existing LPDDR memory in most modern laptops. This type of memory is typically soldered onto the motherboard, making it much more difficult to upgrade. LPCAMM2 or Low-Power Compression-Attached Memory Module offers modularity without losing the power efficiency of LPDDR.Read Article >Apr 23, 2024Sean HollisterFramework wont be just a laptop company anymorePhoto by Monica Chin / The VergeToday, Framework is the modular repairable laptop company. Tomorrow, it wants to be a consumer electronics company, period. Thats one of the biggest reasons it just raised another $18 million in funding it wants to expand beyond the laptop into additional product categories.Framework CEO Nirav Patel tells me that has always been the plan. The company originally had other viable ideas beyond laptops, too. We chose to take on the notebook space first, he says, partly because Framework knew it could bootstrap its ambitions by catering to the PC builders and tinkerers and Linux enthusiasts left behind by big OEMs and partly because it wanted to go big or go home.Read Article >Apr 15, 2024Sean HollisterThe Framework Laptop has a firmware update problem but maybe not for long?I didnt realize itd failed to deliver so many updates til I read this Ars Technica story. The good news: hardware partner Compal now has a whole firmware team ready to go:The goal, Patel says, is to continuously cycle through all of Frameworks actively supported laptops, updating each of them one at a time before looping back around and starting the process over again. Functionality-breaking problems and security fixes will take precedence, while additional features and user requests will be lower-priority.FYI, Framework didnt send me that promised Laptop 16 replacement to show itd fixed issues before launch. Hope so!Frameworks software and firmware have been a mess, but its working on them[Ars Technica]Feb 21, 2024Wes DavisFramework is selling a cheap modular laptopA picture of the Framework 13 from The Verges 2021 review. Photo by Monica Chin / The VergeFramework is now selling a $499 version of its modular 2021 Laptop 13, a barebones configuration equipped with an 11th-generation Intel i7-1165G7 CPU (Intel is now on its 14th generation for mobile processors). The company says this is a first for its affordable B-stock Factory Seconds machines that use leftover parts and ship without memory or storage included. So its cheap, but youll need to provide a couple of parts on your own.Framework writes in its announcement that its also selling refurbished DDR4 RAM for half what it would cost new to reduce the all-in price. The base B-stock Laptop 13 configuration is one step up from the version that Monica Chin said in her Verge review was uniquely friendly to user upgrades, but notably lacked available screen or GPU upgrades.Read Article >Jan 25, 2024Sean HollisterFramework will send us another Framework Laptop 16 and heres what it will fix.We told Framework we had several different stability issues, so I was a little frustrated to see the companys CEO suggest I only encountered one... but Im happy to say Framework will send The Verge a final production unit with quite a list of hardware and software fixes!The DPC_Watchdog_Violation freeze, at least, does seem to be fixed in my testing with a newer BIOS.Framework total list of issues that weve resolved on customer units[u/cmonkey (Reddit)]Jan 23, 2024Sean HollisterFramework Laptop 16 review: two weeks with the ultimate modular laptopThe Framework Laptop 16. Photo by Vjeran Pavic / The VergeThe Framework Laptop 16 is the most ambitious notebook Ive ever touched. Theres never been anything like it before. Theres so much to cover that it wont fit in this review, so Im hosting an AMA today on The Verge to answer your burning questions.Never has a computer company made a laptop so user-repairable, so customizable, so modular. Never have I owned a laptop thats a workhorse by day, physically transforms into a competent gaming PC by night, then morphs into an RGB-LED-studded five-screen DJ controller when the mood strikes. Never have I swapped out a laptops entire butt for a bigger one containing a discrete graphics card, then powered the whole rig with a worlds first 180-watt USB-C PD power supply. And never have I been able to lift out a laptops keyboard and touchpad, shift them to the left or right, then add a numpad, or a matrix of dazzling LEDs, or a simple pop of color alongside. Read Article >Jan 23, 2024Sean HollisterAMA: I reviewed the Framework Laptop 16, ask me anything at 12PM ET!I am unreasonably excited about the ridiculously modular Framework Laptop 16. I did loads of testing that didnt quite fit into my review, so... Ill answer your burning questions at 12PM ET and throughout the day, and you can begin adding em now! Hit that comments button and youll see our Q&A module.Dont be shy: Ive got this machine right here and am happy to quickly test and measure things for you.Oct 11, 2023Monica ChinFramework Laptop 13 (AMD) review: buy this one if you canThe competition between Intel and AMD is a tale as old as time. And here, in the unobtrusive and unsuspecting Framework Laptop 13, that competition comes to a head. Because, for the first time, this modular 13-inch machine allows you to fully swap an Intel processor with an AMD processor (or, I do suppose, vice versa). Itll cost you as little as $449 (the price of a Ryzen 5 mainboard) and half an hour of your time.Its great that an AMD Framework exists, both for folks who already own an Intel version but want to upgrade and for those who are shopping for the first time and will benefit from more choice. Its also great for me personally because it creates a controlled experiment. It allows me to put two competing chips side by side in a literally identical chassis and test them out. Theres not even a price differential: the two systems are the same price, with prebuilts starting at $1,049 and DIY kits at $849 in both cases.Read Article >Oct 6, 2023Sean HollisterLenovo exec promises 80 percent of its devices will be consumer-repairable by 2025Lenovos rollable laptop concept. Photo by Jon Porter / The VergeAt first, Lenovo only seemed casually jealous of Frameworks modular repairable laptops first, it sent a cease-and-desist over a Framework power button, then it unveiled its own modular concept laptop dubbed Project Aurora with no promise to actually build such a thing.But it looks like the ThinkPad and Motorola owner might actually be serious about ramping up repairability. More than 80 percent of our devices will be able to be repaired at the customer, Lenovo executive Luca Rossi told the Canalys EMEA Forum 2023, according to The Register.Read Article >Sep 8, 2023Sean HollisterFramework is finally working on a full-size SD card moduleThis is not Frameworks module but rather a community design by zero0d. Image: zero0dYou can hot-swap a DisplayPort, ethernet, even an extra 3.5mm audio jack into your modular Framework laptop and today, the company is finally beginning work on a full-size SD Expansion Card to go with them.But seriously, its just beginning that work: the company says its breaking tradition by pre-announcing a module that may never ship. Weve set a target for what we want it to be, but as we proceed and learn, theres a chance it could change or even be canceled, Framework writes. Dont worry: its not trying to cash in ahead of time; the idea is by doing it this way, Framework can take you behind the scenes.Read Article >Jul 19, 2023Sean HollisterHow Dell dodged a class action suit over Alienware Area-51m GPU upgradesThe Alienware Area-51m. Photo by Vjeran Pavic / The VergeIn 2021, Dell got sued because one year after hyping up an Alienware laptop that supposedly let you upgrade its discrete GPU, the company didnt follow through.The story of the Alienware Area-51m is pretty dang relevant today because Framework just opened preorders for another laptop that offers the same.Read Article >Jul 19, 2023Tom WarrenIntel mini NUC computers get a second life thanks to AsusImage: IntelIntel announced earlier this week that its compact and upgradable NUC computers were being discontinued, but now Asus is stepping in to manufacture and develop future NUC systems instead. While Intel wont be making its cute small form factor PCs anymore, Asus will receive a non-exclusive license to Intels NUC (Next Unit of Computing) product designs.As we pivot our strategy to enable ecosystem partners to continue NUC systems product innovation and growth, our priority is to ensure a smooth transition for our customers and partners, says Sam Gao, general manager of Intel Client Platform Solutions. I am looking forward to ASUS continuing to deliver exceptional products and supporting our NUC systems customers.Read Article >Jul 17, 2023Umar ShakirLenovo ponders repairable laptops with Project Aurora.Digital Trends got a preview of Lenovos Aurora design that comes apart without the use of so much as a screwdriver. The company is the latest to look towards sustainable notebook designs following the successful release of Frameworks modular laptops. Theyre a DIY dream come true thanks to easy upgrades to basically any component, which as the Framework 16 now includes graphics too.Dell also announced a similar Project Luna concept late last year.Lenovos Project Aurora concept. Image: Digital Trends / LenovoJul 11, 2023Umar ShakirIntel is quitting on its adorable, powerful, and upgradable mini NUC computersThis Intel NUC 9 Extreme was one of the larger of the mini-computer line and had more gaming horsepower. Photo by Dan Seifert / The VergeIntels NUC computers are super compact, upgradable, and even powerful but now, theyre being discontinued. ServeTheHome first reported that Intel is giving up on the personal computer business and will no longer be making its cute small form factor PCs.In an email to The Verge, Intels EMEA comms manager of client computing and graphics, Mark Walton, confirmed the news and issued the following statement:Read Article >May 17, 2023Monica ChinFramework Laptop 13 review: a DIY dream come trueFramework has released a 2023 version of its 13-inch modular laptop. And folks, it is exactly the same as the 2022 model.Okay, so, I mean, thats not quite true. There is an AMD option now, but I dont yet have that one to review. Ive been testing the pre-built Intel model, which includes a 13th Gen Core i7-1360P. The battery is also larger, coming in at 61Wh. There are a couple other small things about the chassis. Theres a new matte display option and a new speaker system. But I am going to tell you right now that using the 2023 Framework Laptop 13 feels exactly like using the 2022 Framework Laptop. The experiences are basically identical.Read Article >May 3, 2023Monica ChinFinally, you can put an AMD processor in the Framework LaptopTwo Frameworks enter... Image: FrameworkFramework has announced the 2023 edition of its modular 13-inch laptop. The big news is that not only is there a 13th-Gen Intel configuration for sale, but theres also an AMD Ryzen 7040 option available. Thats right. Finally, an AMD option.One of the difficulties I had in reviewing last years Framework Laptop was that the Intel processor didnt quite measure up to everything else that was great about the device. Im obsessed with the Framework as a concept whats not to love about a repairable, fully upgradable notebook? but as a daily driver, it was a bit unremarkable, and battery life was particularly disappointing.Read Article >Apr 2, 2023Dan SeifertFrameworks computers arent perfect, but they are excitingThe new Framework Laptop 16. Image: FrameworkIts not often that I get too excited about new laptops these days. Modern laptops are extremely capable devices, with few glaring flaws. They are thin, light, and finely tuned to get the job done. Exciting, they are not.But Frameworks laptops are exciting. Under the banner of repairability and sustainability, Framework is making computers that seem to be exactly what enthusiasts have been asking for for literal decades. Nearly every part of a Framework Laptop can be repaired, replaced, or upgraded by its owner. Want a faster CPU or more RAM? Just swap the board and click in some more RAM sticks, and youre off to the races. The company is even coming out with a gaming-focused laptop that promises the ability to upgrade its GPU down the line. Read Article >More Stories

Apps distributed through both Apple and Googles app stores are hiding malicious screenshot-reading code thats being used to steal cryptocurrency, the cybersecurity software firm Kaspersky reported today. Its the first known case of apps infected with malware that uses OCR tech to extract text from images making it into Apples App Store, according to a blog post detailing the companys findings.Kaspersky says it discovered the code from this particular malware campaign, which it calls SparkCat, in late 2024 and that the frameworks for it appear to have been created in March of the same year. On iOS and in some Android instances, the malware works by triggering a request to access users photo galleries when they attempt to use chat support within the infected app. Once permission is granted, it uses Google OCR tech, which lets it decipher text found in photos, to look for things like screenshots of crypto wallet passwords or recovery phrases. The software then sends any images it finds back to the attackers, who can then use the info to access the wallets and steal crypto.Kaspersky says it cant confirm with certainty the infection was a result of a supply chain attack or deliberate action by the developers. The company names two AI chat apps that seem to have been created for the campaign and appear to still be available on the App Store, called WeTink and AnyGPT. Additionally, Kaspersky found the malicious code in a legitimate-seeming food delivery app called ComeCome, which you can also still download.Neither Apple nor Google immediately responded to The Verges request for comment.

GameFromScratch.comLua Game Engines in 2025Today we have compiled a list of the best Lua game engines and game frameworks (and a few miscellaneous game development options) for game development in 2025. This is the third in a series of posts exploring the game engine options available for the most popular programming languages. We haveThe post Lua Game Engines in 2025 appeared first on GameFromScratch.com.

Large Language Models (LLMs) have demonstrated notable reasoning capabilities in mathematical problem-solving, logical inference, and programming. However, their effectiveness is often contingent on two approaches: supervised fine-tuning (SFT) with human-annotated reasoning chains and inference-time search strategies guided by external verifiers. While supervised fine-tuning offers structured reasoning, it requires significant annotation effort and is constrained by the quality of the teacher model. Inference-time search techniques, such as verifier-guided sampling, enhance accuracy but increase computational demands. This raises an important question: Can an LLM develop reasoning capabilities independently, without relying on extensive human supervision or external verifiers? To address this, researchers have introduced Satori, a 7B parameter LLM designed to internalize reasoning search and self-improvement mechanisms.Introducing Satori: A Model for Self-Reflective and Self-Exploratory ReasoningResearchers from MIT, Singapore University of Technology and Design, Harvard, MIT-IBM Watson AI Lab, IBM Research, and UMass Amherst propose Satori, a model that employs autoregressive searcha mechanism enabling it to refine its reasoning steps and explore alternative strategies autonomously. Unlike models that rely on extensive fine-tuning or knowledge distillation, Satori enhances reasoning through a novel Chain-of-Action-Thought (COAT) reasoning paradigm. Built upon Qwen-2.5-Math-7B, Satori follows a two-stage training framework: small-scale format tuning (FT) and large-scale self-improvement via reinforcement learning (RL).Technical Details and Benefits of SatoriSatoris training framework consists of two stages:Format Tuning (FT) Stage:A small-scale dataset (~10K samples) is used to introduce COAT reasoning, which includes three meta-actions:Continue (<|continue|>): Extends the reasoning trajectory.Reflect (<|reflect|>): Prompts a self-check on previous reasoning steps.Explore (<|explore|>): Encourages the model to consider alternative approaches.Unlike conventional CoT training, which follows predefined reasoning paths, COAT enables dynamic decision-making during reasoning.Reinforcement Learning (RL) Stage:A large-scale self-improvement process using Reinforcement Learning with Restart and Explore (RAE).The model restarts reasoning from intermediate steps, refining its problem-solving approach iteratively.A reward model assigns scores based on self-corrections and exploration depth, leading to progressive learning.InsightsEvaluations show that Satori performs strongly on multiple benchmarks, often surpassing models that rely on supervised fine-tuning or knowledge distillation. Key findings include:Mathematical Benchmark Performance:Satori outperforms Qwen-2.5-Math-7B-Instruct on datasets such as GSM8K, MATH500, OlympiadBench, AMC2023, and AIME2024.Self-improvement capability: With additional reinforcement learning rounds, Satori demonstrates continuous refinement without additional human intervention.Out-of-Domain Generalization:Despite training primarily on mathematical reasoning, Satori exhibits strong generalization to diverse reasoning tasks, including logical reasoning (FOLIO, BoardgameQA), commonsense reasoning (StrategyQA), and tabular reasoning (TableBench).This suggests that RL-driven self-improvement enhances adaptability beyond mathematical contexts.Efficiency Gains:Compared to conventional supervised fine-tuning, Satori achieves similar or better reasoning performance with significantly fewer annotated training samples (10K vs. 300K for comparable models).This approach reduces reliance on extensive human annotations while maintaining effective reasoning capabilities.Conclusion: A Step Toward Autonomous Learning in LLMsSatori presents a promising direction in LLM reasoning research, demonstrating that models can refine their own reasoning without external verifiers or high-quality teacher models. By integrating COAT reasoning, reinforcement learning, and autoregressive search, Satori shows that LLMs can iteratively improve their reasoning abilities. This approach not only enhances problem-solving accuracy but also broadens generalization to unseen tasks. Future work may explore refining meta-action frameworks, optimizing reinforcement learning strategies, and extending these principles to broader domains.Check outthePaper and GitHub Page.All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System (Promoted)The post Meet Satori: A New AI Framework for Advancing LLM Reasoning through Deep Thinking without a Strong Teacher Model appeared first on MarkTechPost.

Reinforcement Learning RL trains agents to maximize rewards by interacting with an environment. Online RL alternates between taking actions, collecting observations and rewards, and updating policies using this experience. Model-free RL (MFRL) maps observations to actions but requires extensive data collection. Model-based RL (MBRL) mitigates this by learning a world model (WM) for planning in an imagined environment. Standard benchmarks like Atari-100k test sample efficiency, but their deterministic nature allows memorization rather than generalization. To encourage broader skills, researchers use Crafter, a 2D Minecraft-like environment. Craftax-classic, a JAX-based version, introduces procedural environments, partial observability, and a sparse reward system, requiring deep exploration.MBRL methods vary based on how WMs are usedfor background planning (training policies with imagined data) or decision-time planning (conducting lookahead searches during inference). As seen in MuZero and EfficientZero, decision-time planning is effective but computationally expensive for large WMs like transformers. Background planning, originating from Dyna-Q learning, has been refined in deep RL models like Dreamer, IRIS, and DART. WMs also differ in generative ability; while non-generative WMs excel in efficiency, generative WMs better integrate real and imagined data. Many modern architectures use transformers, though recurrent state-space models like DreamerV2/3 remain relevant.Researchers from Google DeepMind introduce an advanced MBRL method that sets a new benchmark in the Craftax-classic environment, a complex 2D survival game requiring generalization, deep exploration, and long-term reasoning. Their approach achieves a 67.42% reward after 1M steps, surpassing DreamerV3 (53.2%) and human performance (65.0%). They enhance MBRL with a robust model-free baseline, Dyna with warmup for real and imagined rollouts, a nearest-neighbor tokenizer for patch-based image processing, and block teacher forcing for efficient token prediction. These refinements collectively improve sample efficiency, achieving state-of-the-art performance in data-efficient RL.The study enhances the MFRL baseline by expanding the model size and incorporating a Gated Recurrent Unit (GRU), increasing rewards from 46.91% to 55.49%. Additionally, the study introduces an MBRL approach using a Transformer World Model (TWM) with VQ-VAE quantization, achieving 31.93% rewards. To further optimize performance, a Dyna-based method integrates real and imagined rollouts, improving learning efficiency. Replacing VQ-VAE with a patch-wise nearest-neighbor tokenizer boosts performance from 43.36% to 58.92%. These advancements demonstrate the effectiveness of combining memory mechanisms, transformer-based models, and improved observation encoding in reinforcement learning.The study presents results from experiments on the Craftax-classic benchmark, conducted on 8 H100 GPUs over 1M steps. Each method collected 96-length trajectories in 48 parallel environments. For MBRL methods, imaginary rollouts were generated at 200k environment steps and updated 500 times. The MBRL ladder progression showed significant improvements, with the best agent (M5) achieving a 67.42% reward. Ablation studies confirmed the importance of each component, such as Dyna, NNT, patches, and BTF. Compared with existing methods, the best MBRL agent achieved a state-of-the-art performance. Additionally, Craftax Full experiments demonstrated generalization to harder environments.In conclusion, the study introduces three key improvements to vision-based MBRL agents using TWM for background planning. These enhancements include Dyna with warmup, patch nearest-neighbor tokenization, and block teacher forcing. The proposed MBRL agent performs better on the Craftax-classic benchmark, surpassing previous state-of-the-art models and human expert rewards. Future work includes exploring generalization beyond Craftax, prioritizing experience replay, integrating off-policy RL algorithms, and refining the tokenizer for large pre-trained models like SAM and Dino-V2. Additionally, the policy will be modified to accept latent tokens from non-reconstructive world models.Check outthePaper.All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Deep Agent Released R1-V: Reinforcing Super Generalization in Vision-Language Models with Cost-Effective Reinforcement Learning to Outperform Larger ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/ARM: Enhancing Open-Domain Question Answering with Structured Retrieval and Efficient Data AlignmentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Google AI Introduces Parfait: A Privacy-First AI System for Secure Data Aggregation and AnalyticsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Exploration Challenges in LLMs: Balancing Uncertainty and Empowerment in Open-Ended Tasks [Recommended] Join Our Telegram Channel

Author(s): Michael Ryaboy Originally published on Towards AI. Image Source https://github.com/chonkie-ai/Late Chunking has just been released in Chonkie, a lean chunking library that already boasts over 2,000 stars on GitHub. This is a welcome update for anyone looking to integrate late chunking into their retrieval pipelines, since implementing it from the ground up can be conceptually tricky and prone to mistakes. This article breaks down what Late Chunking is, why its essential for embedding larger or more intricate documents, and how to build it into your search pipeline using Chonkie and KDB.AI as the vector store.What is Late Chunking?When you have a document that spans thousands of words, encoding it into a single embedding often isnt optimal. In many scenarios, you need to retrieve smaller segments of text, and dense-vector retrieval tends to perform better when those text segments (chunks) are smaller. This is partly because embedding a whole, massive document may over-compress its semantics into a single vector.Retrieval-Augmented Generation (RAG) is a prime example that benefits from splitting documents into smaller text chunks often around 512 tokens each. In RAG, you store these chunks in a vector database and encode them with a text embedding model.The Lost Context ProblemThe typical RAG pipeline of chunk embed retrieve generate is far from perfect. Splitting text naively can inadvertently break longer contextual relationships. If crucial information is spread across multiple chunks, or a chunk requires context from the wider document, simply retrieving one chunk alone might not provide enough context to answer a query accurately. Our chunk embeddings also do not represent the chunks full meaning, which means the correct chunks might not be retrieved.Take, for instance, a query like:What is the population of Berlin?If an article is split sentence by sentence, one chunk might mention Berlin, while another mentions the population figure without restating the city name. Without the context from the entire document, these fragments cant answer the query effectively, especially when resolving references like it or the city. This example by Jina AI demonstrates this further:Late Chunking SolutionInstead of passing each chunk individually to an embedding model, in Late Chunking:The entire text (or as much as possible) is processed by the transformer layers of your embedding model, generating token embeddings that reflect global context.Text is split into chunks, and mean pooling is applied to token embeddings within each chunk to create embeddings informed by the whole document.This preserves document context in every chunk, ensuring the embedding captures more than just the local semantics of the individual chunk. Of course, this doesnt solve the issue of the chunk itself not having enough context. To solve this, check out my article comparing Late Chunking to Contextual Retrieval, a method popularized by Anthropic to add context to chunks with LLMs:https://medium.com/kx-systems/late-chunking-vs-contextual-retrieval-the-math-behind-rags-context-problem-d5a26b9bbd38.In practice, what this does instead is reduce the number of failed retrievals, and clusters chunk embeddings around the document.Naive vs Late Chunking ComparisonLate Embedding Process. Image By Author.In a naive approach, each chunk is encoded independently, producing embeddings that lack context from other chunks. Late Chunking, on the other hand, creates chunk embeddings conditioned on the global context, significantly improving retrieval performance. This helps reduce hallucinations and failed responses in RAG systems.Late chunking has been shown to improve retrieval performance, which in turn means it can reduce RAG hallucinations and failed responses.Implementation with Chonkie and KDB.AIImage Source: KDB.AIHeres how you can implement Late Chunking using KDB.AI as the vector store.(Disclaimer, Im a Developer Advocate for KDB.AI and a contributor to Chonkie.)1. Install Dependencies and Set Up LateChunker!pip install "chonkie[st]" kdbai-client sentence-transformersfrom chonkie import LateChunkerimport kdbai_client as kdbaiimport pandas as pd# Initialize Late Chunkerchunker = LateChunker( embedding_model="all-MiniLM-L6-v2", mode="sentence", chunk_size=512, min_sentences_per_chunk=1, min_characters_per_sentence=12,)2. Set Up the Vector DatabaseYou can sign up for a free-tier KDB.AI instance at kdb.ai, which offers up to 4 MB memory and 32 GB storage. This is more than enough for most use cases if embeddings are stored efficiently.# Initialize KDB.AI sessionsession = kdbai.Session( api_key="your_api_key", endpoint="your_endpoint")# Create database and define schemadb = session.create_database("documents")schema = [ {"name": "sentences", "type": "str"}, {"name": "vectors", "type": "float64s"},]# Configure HNSW index for fast similarity searchindexes = [{ 'type': 'hnsw', 'name': 'hnsw_index', 'column': 'vectors', 'params': {'dims': 384, 'metric': "L2"},}]# Create tabletable = db.create_table( table="chunks", schema=schema, indexes=indexes)3. Chunk and EmbedHeres an example using Paul Grahams essays in Markdown format. Well generate late chunks and store them in the vector database.import requestsurls = ["ww.paulgraham.com/wealth.html", "www.paulgraham.com/start.html"]texts = [requests.get('http://r.jina.ai/' + url).text for url in urls]batch_chunks = chunker(texts)chunks = [chunk for batch in batch_chunks for chunk in batch]# Store in KDB.AIembeddings_df = pd.DataFrame({ "vectors": [chunk.embedding.tolist() for chunk in chunks], "sentences": [chunk.text for chunk in chunks]})embeddings_df.head()4. Query the Vector StoreLets test the retrieval pipeline by embedding a search query and finding the most relevant chunks.import sentence_transformerssearch_query = "to get rich do this"search_embedding = sentence_transformers.SentenceTransformer("all-MiniLM-L6-v2").encode(search_query)# search for similar documentstable.search(vectors={'hnsw_index': [search_embedding]}, n=3)[0]['sentences']And we are able to get some results! The results arent ideal, as the dataset size is tiny, we are using a weak embedding model, and we arent utilizing reranking. But as the size of the dataset scales, late chunking can give a very significant boost in accuracy.5. Clean UpRemember to drop the database to save resources:db.drop()ConclusionLate Chunking solves the critical issue of preserving long-distance context in retrieval pipelines. When paired with KDB.AI, you get:Context-aware embeddings: Every chunks embedding reflects the entire document.Sub-100ms latency: Leveraging KDB.AIs HNSW index ensures fast retrieval.Scalability: Capable of handling large-scale datasets in production.Chonkie makes adding Late Chunking to your pipeline extremely simple. If youve struggled with building this from scratch before (like I have), this library will definitely save you a lot of time and headaches.For more insights into advanced AI techniques, vector search, and Retrieval-Augmented Generation, follow me on Linkedin!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

LatestMachine LearningLLaMA Architecture: A Deep Dive into Efficiency and Mathematics 0 like February 5, 2025Share this postLast Updated on February 5, 2025 by Editorial TeamAuthor(s): Anay Dongre Originally published on Towards AI. LLaMA Architecture: A Deep Dive into Efficiency and MathematicsIn recent years, transformer-based large language models (LLMs) have revolutionized natural language processing (NLP). Meta AIs LLaMA (Large Language Model Meta AI) stands out as one of the most efficient and accessible models in this domain. LLaMAs design leverages innovations in transformer architecture to achieve competitive performance with fewer parameters, making it more accessible for researchers and businesses with limited computational resources. This article provides an in-depth exploration of the LLaMA architecture, including its mathematical foundations, architectural innovations (such as rotary positional embeddings), and production-level training code on a small dataset using PyTorch.We begin with an overview of the transformer architecture before delving into LLaMA-specific modifications. We then walk through the mathematics behind self-attention, rotary positional embeddings, and normalization techniques used in LLaMA. Finally, we present a complete training pipeline code that demonstrates fine-tuning an LLaMA-like model on a custom dataset.No official image found for LLaMa architecture1. Background: The Transformer Architecture1.1 OverviewTransformers, introduced by Vaswani et al. in 2017, transformed NLP by enabling parallel processing and capturing long-range dependencies without recurrent structures. The key components of a transformer are:Self-Attention Mechanism: Allows each token in a sequence to weigh the importance of every other token.Feedforward Neural Network (FFN): Applies non-linear transformations to the outputs of the self-attention layer.Layer Normalization and Residual Connections: Ensure stable gradient flow and efficient training.Mathematically, for an input sequence represented by a matrix X (of shape nd for sequence length n and embedding dimension d), the self-attention mechanism is computed as:where:dk is the dimension of the key vectors.This formulation allows the model to focus on different parts of the input sequence simultaneously, capturing both local and global relationships.1.2 Limitations of Standard TransformersWhile powerful, standard transformers have some challenges:High Computational Cost: Especially when scaling to large sequences.Fixed Positional Encodings: Typically, absolute positional encodings may not generalize well for very long contexts.Memory Footprint: Large parameter counts require significant computational resources.2. LLaMA Architecture: Innovations and ImprovementsLLaMA builds upon the standard transformer architecture while introducing several key optimizations designed to improve efficiency and scalability.2.1 Decoder-Only Transformer DesignLLaMA uses a decoder-only transformer architecture similar to GPT models. In this design, the model generates text in an autoregressive manner predicting one token at a time given all previous tokens. This choice simplifies the architecture by focusing on language modeling without the need for an encoder.2.2 Pre-NormalizationInstead of the traditional post-norm (LayerNorm after sub-layers), LLaMA employs pre-normalization, where LayerNorm is applied before the self-attention and feedforward layers. Mathematically, if x is the input to a sub-layer (e.g., attention), the transformation is:This approach improves training stability, especially for very deep networks, by ensuring that the input to each sub-layer has a standardized scale.2.3 Rotary Positional Embeddings (RoPE)One of the hallmark features of LLaMA is its use of rotary positional embeddings (RoPE). Unlike traditional absolute positional embeddings, RoPE encodes relative positions of tokens in a mathematically elegant way.Mathematical Explanation of RoPEFor each token, instead of simply adding a fixed vector, RoPE rotates the query and key vectors in a multi-dimensional space according to their position. If is a rotation angle that is a function of the token position p and a base frequency , then a vector x is rotated as:Here, the function rotate(x) represents a 90-degree rotation in the embedding space. This method has two key benefits:Scalability: It generalizes well to longer sequences because the relative angle between tokens remains consistent.Efficiency: No additional parameters are needed compared to learned positional embeddings.Simplified ExplanationImagine you have a set of vectors that represent words, and you want to know not just the word identities but also their order. RoPE rotates these vectors by an angle proportional to their position. When you compare two tokens, the relative angle (difference in rotation) encodes their distance in the sequence, which is essential for understanding context.2.4 Parameter Efficiency and Grouped-Query Attention (GQA)LLaMA optimizes parameter usage through techniques like grouped-query attention (GQA). This mechanism partitions the query vectors into groups that share certain parameters, thereby reducing the overall number of computations and memory footprint without significantly compromising performance. The mathematics here is an extension of standard multi-head attention, where instead of independent heads, groups of heads share projections:where g indexes groups. This sharing enables the model to maintain a high degree of expressiveness while lowering the parameter count.3. LLaMA in Practice: Autoregressive Text Generation3.1 Autoregressive Generation ProcessLLaMA, like other decoder-only models, uses an autoregressive method to generate text. At each step, the model:Takes the current sequence of tokens.Computes the self-attention over all tokens.Predicts the next token using a softmax layer over the vocabulary.Mathematically, if x1:t represents the sequence, then the probability of the next token xt+1 is:where f represents the transformers forward pass. The process repeats until a termination token is generated.3.2 Example ScenarioConsider the input prompt:The capital of France isLLaMA processes the tokens through multiple transformer blocks. Using its autoregressive nature, it predicts the next token with the highest probability (e.g., Paris), appends it to the sequence, and continues generating further tokens until the sentence is complete.4. Mathematical Foundations SimplifiedLets break down the key mathematical concepts in simpler terms:4.1 Self-Attention RevisitedThe self-attention mechanism calculates relationships between tokens. Imagine you have a sentence: The cat sat on the mat. For each word, the model computes:Query (what this word is asking for)Key (what this word offers)Value (the content of this word)The similarity between words is computed as a dot product of queries and keys. Dividing by sqrt{d_k} (a scaling factor) prevents the numbers from becoming too large. The softmax function then converts these scores into probabilities (weights) that sum to 1. Finally, these weights multiply the value vectors to produce a weighted sum, which becomes the output for that token.4.2 Rotary Positional Embeddings (RoPE)RoPE mathematically rotates each words vector based on its position. Think of each word vector as an arrow in space. By rotating these arrows, the model can encode how far apart words are. When two arrows are compared, the difference in rotation tells you the relative distance between words. This is essential for understanding sentence structure without needing extra parameters for each position.4.3 Pre-NormalizationIn pre-normalization, every input to a sub-layer is normalized before processing. This means the data is scaled so that its mean is zero and its variance is one. Mathematically, given an input xxx, the normalized value x^ is:x^=x / +where:is the mean of x, is the standard deviation, is a small constant to avoid division by zero.By normalizing the input, the network ensures that the scale of the data does not vary too much from layer to layer, which helps in faster and more stable training.5. Production Considerations and OptimizationsWhen deploying or fine-tuning LLaMA models in production, consider the following:5.1 Data Preprocessing Normalization and Cleaning: Ensure that input texts are cleaned (e.g., removing HTML tags, extra whitespace). Tokenization: Use the tokenizer associated with your model to ensure consistency.5.2 Training Infrastructure GPU/TPU Usage: Leverage distributed training if using large datasets. Checkpointing: Regularly save checkpoints to avoid loss of progress.5.3 Hyperparameter Tuning Learning Rate Schedules: Experiment with warmup and decay schedules. Regularization: Techniques such as dropout or weight decay are crucial to avoid overfitting. Batch Size and Gradient Accumulation: Adjust based on hardware capabilities.5.4 Monitoring and Evaluation Logging: Use tools like TensorBoard to monitor loss and other metrics. Validation Metrics: Regularly evaluate using a validation set to check for overfitting. Error Analysis: Analyze model errors to guide further improvements.5.5 Deployment Model Compression: Techniques like quantization or distillation can reduce model size for deployment. API Endpoints: Use frameworks such as FastAPI or Flask for serving your model in production. Scaling: Consider cloud solutions (e.g., AWS, GCP) to scale inference services.ReferencesVaswani et al., Attention Is All You Need (2017):2. Rotary Positional Embeddings Paper (Su et al., 2021):3. LLaMA: Open and Efficient Foundation Language ModelsLink: https://arxiv.org/abs/2302.139714. The Llama 3 Herd of ModelsLink: https://arxiv.org/abs/2407.217835. Llama 2: Open Foundation and Fine-Tuned Chat ModelsLink: https://arxiv.org/abs/2307.09288Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Pokmon Trading Card Game Pocket is estimated to have made a staggering half a billion dollars in less than three months, the same amount popular culture hit Pokmon Go made in just 22 days fewer.PocketGamer.biz cited figures from AppMagic that estimated the digital card game hit $500 million on February 4, meaning just 97 days after it launched. It's now just the second Pokmon mobile game to hit that number, behind only the aforementioned Pokmon Go.The release of the Space Time Smackdown expansion saw daily sales skyrocket to more than $10 million for the first time, with the two days it was available in January accounting for 22% of the entire month's revenue.Space Time Smackdown, at 207 cards, was the first full expansion for Pokmon TCG Pocket, which launched with its debut, 286 card Genetic Apex set in October before releasing a smaller, 86 card set called Mythical Island in December. Developer Creatures Inc. plans to continue releasing sets in this manner, with a large one and a small one in interchanging months.The game follows the standard mobile and free to play game model, flooding players with rewards in the opening few days before soon drying up, with spending real world money the only real way to re-experience that early thrill.Completing Genetic Apex will takes players not spending money around two years according to one estimate, while those looking to make it rain can wrap up the collection after dropping around $1,500.It's not all been Sunfloras and Walrein-bows for Creatures Inc., however, as the developer has been called "predatory" and "downright greedy" over the past week as fans rallied against a poorly received trading feature.Creatures Inc. today gifted players 1,000 Trade Tokens enough for just two significant trades as it continues to investigate ways to fix the controversial mechanic, though fans are still frustrated at the lack of communication and false promises.Ryan Dinsdale is an IGN freelance reporter. He'll talk about The Witcher all day.