Posted on : June 5, 2025 By Tech World Times Technology  Rate this post The Software as a Service (SaaS) industry has been on an unstoppable trajectory since its inception, revolutionizing how businesses and individuals consume and manage software. The landscape of subscription services within this sector continues to evolve at a rapid pace. Driven by customer expectations, technological advancements, and economic shifts, several trends are emerging that promise to reshape the SaaS industry. These trends will not only influence the way software is delivered and managed but also impact the overall subscription economy. Check out these subscription trends that are set to redefine the SaaS industry in the next few years. 1. Personalization and Customer-Centric Models Businesses are increasingly recognizing the value of tailored experiences, and SaaS companies are responding with personalized subscription offerings. By leveraging user data and machine learning algorithms, providers are crafting customized solutions that cater to individual needs, preferences, and usage patterns. Personalization also extends to the billing process, where SaaS payment processing systems are adapting to offer flexible pricing models, such as pay-per-use and tiered subscriptions. 2. Integrated Payment Solutions and Subscription Management SaaS payment processing will become a seamless and integrated part of the subscription experience. Innovations in financial technology will streamline the billing process, providing users with a unified dashboard to manage all their subscriptions. This will simplify financial management for both consumers and businesses. Companies will be able to offer various payment methods, automate billing cycles, and handle currency conversions with ease, making it easier for them to serve a global customer base. 3. The Rise of Usage-Based Pricing Usage-based pricing is gaining traction as a fair and transparent way to bill for cloud services. Instead of flat monthly or annual fees, SaaS providers will charge customers based on the actual resources consumed. This model aligns the cost with the value derived, encouraging more efficient use of resources and reducing waste. It also allows companies to scale more easily, leading to more granular and sophisticated pricing structures that can handle these complex calculations. 4. Subscription Model Innovation To stand out in a crowded market, SaaS companies are experimenting with innovative subscription models that go beyond the typical monthly or annual plans. These include freemium models, where users get a basic version of the software for free and pay for additional features, and dynamic pricing that adjusts based on demand and supply. The goal is to create a more nuanced pricing strategy that appeals to a wider range of customers and enhances the overall perceived value of the service. 5. Emphasis on User Experience and Customer Retention The importance of user experience is paramount in the SaaS industry, and in 2025, companies will invest heavily in creating delightful onboarding processes, intuitive user interfaces, and proactive customer support. The focus on customer retention will be a key driver for subscription growth, as it becomes more cost-effective than acquiring new ones. This shift will lead to the integration of more sophisticated tools for tracking customer engagement and satisfaction, allowing for targeted interventions to reduce churn rates. 6. Expansion into Emerging Markets As developed markets become saturated, SaaS companies are setting their sights on emerging economies. To cater to these diverse markets, providers will need to adapt their offerings to suit local preferences and constraints, such as language, cultural nuances, and payment methods. This trend will result in the rise of regional SaaS leaders and increased competition from local players, pushing the industry to become more global and inclusive. 7. Increased Adoption of AI and Automation Artificial intelligence and automation will play a significant role in the SaaS industry, optimizing everything from customer support to sales and marketing. Chatbots and virtual assistants will become commonplace for handling routine inquiries, while AI algorithms will be used to predict customer behavior and offer personalized recommendations. Automation will streamline backend processes, reducing costs and enhancing the overall efficiency of subscription management. This integration will lead to more intelligent and proactive services, with the potential for predictive analytics to anticipate customer needs before they even arise. The SaaS industry is poised for continued growth and evolution, with these seven subscription trends at the forefront. Personalization, integrated payment processing, usage-based pricing, innovative models, user experience enhancements, emerging market expansion, and the integration of AI and automation are all shaping the future of this dynamic sector. The most successful SaaS companies will be those that embrace these trends, adapting their offerings and operations to meet the changing demands of the modern subscription economy. With the right strategies in place, they will be able to deliver superior value to their customers and thrive in an increasingly competitive landscape. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Volvo is introducing a new seatbelt that self-adjusts based on your body and driving conditions, particularly in the event of a collision.This "multi-adaptive safety belt" gathers real-time data from interior and exterior sensors, adapting the force it applies "based on the situation and individual’s profiles, such as their height, weight, body shape and seating position," Volvo says. Multi-adaptive safety belt(Credit: Volvo)It's a way of customizing protection for the rider, rather than a static setting for all passengers. Volvo gives the example of a larger occupant and a smaller occupant during a car crash. The smaller will need a "lower belt load" to reduce the risk of rib fractures, while the larger one will need a "higher belt load" to reduce the risk of head injury. Recommended by Our EditorsMost seatbelts have three settings, or "load-limiting profiles," but Volvo offers 11. The sensors analyze the unique characteristics of a crash "in less than a blink of the eye," such as direction, speed, and passenger posture, and shares that information with the seatbelt. It uses this data to select the appropriate setting.Multi-adaptive safety belt(Credit: Volvo)Volvo says it developed the tech with data from 80,000 riders and real accidents, and is approaching the seatbelt as a piece of living tech with the capability to receive over-the-air software updates and continuously improve based on data gathered from all Volvo cars. While it could significantly improve safety, more tech can also mean more room to malfunction. If the belt gets too tight, or too loose, it could be a headache for riders.The new belt debuts later this year in the all-electric 2026 Volvo EX60, a midsize SUV set to compete with the Tesla Model Y. Volvo hasn't revealed pricing or photos of the vehicle yet, but it's expected to be among the most popular in the brand's EV lineup. That includes the EX30, a smaller SUV, three-row EX90, and ES90 sedan.Volvo EX90 on the Las Vegas strip(Credit: Emily Forlini/PCMag)

html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd" The MetaHuman client reel. Epic Games’ framework for generating realistic 3D characters for games is out of early access, and can now be used with any DCC app or game engine. Epic Games has officially launched MetaHuman, its framework for generating realistic 3D characters for games, animation and VFX work, after four years in early access.The core applications, MetaHuman Creator, Mesh to MetaHuman and MetaHuman Animator, are now integrated into Unreal Engine 5.6, the latest version of the game engine. In addition, Epic has updated the licensing for MetaHuman characters, making it possible to use them in any game engine or DCC application, including in commercial projects. There are also two new free plugins, MetaHuman for Maya and MetaHuman for Houdini, intended to streamline the process of editing MetaHumans in Maya and Houdini. A suite of tools for generating and animating realistic real-time 3D characters First launched in early access in 2021, MetaHuman is a framework of tools for generating realistic 3D characters for next-gen games, animation, virtual production and VFX.The first component, MetaHuman Creator, enables users to design realistic digital humans. Users can generate new characters by blending between presets, then adjusting the proportions of the face by hand, and customising readymade hairstyles and clothing. The second component, Mesh to MetaHuman, makes it possible to create MetaHumans matching 3D scans or facial models created in other DCC apps. The final component, MetaHuman Animator, streamlines the process of transferring the facial performance of an actor from video footage to a MetaHuman character. MetaHuman Creator was originally a cloud-based tool, while Mesh to MetaHuman and MetaHuman Animator were available via the old MetaHuman plugin for Unreal Engine. Now integrated directly into Unreal Engine 5.6 That changes with the end of early access, with MetaHuman Creator, Mesh to MetaHuman and MetaHuman Animator all now integrated directly into Unreal Engine itself.Integration – available in Unreal Engine 5.6, the latest version of the engine – is intended to simplify character creation and asset management worklows. Studios also get access to the MetaHuman source code, since Unreal Engine itself comes with full C++ source code access. However, the tools still cannot be run entirely locally: according to Epic, in-editor workflow is “enhanced by cloud services that deliver autorigging and texture synthesis”. https://www.cgchannel.com/wp-content/uploads/2025/06/250604_MetaHumanOfficialLaunch_LicensingChanges_UnifiedClothing.mp4 Users can now adjust MetaHumans’ bodies, with a new unified Outfit Asset making it possible to create 3D clothing that adjusts automatically to bodily proportions. Updates to both MetaHuman Creator and MetaHuman Animator In addition, the official release introduces new features, with MetaHuman Creator’s parametric system for creating faces now extended to body shapes.Users can now adjust proportions like height, chest and waist measurements, and leg length, rather than simply selecting preset body types. Similarly, a new unified Outfit Asset makes it possible to author custom 3D clothing, rather than selecting readymade presets, with garments resizing to characters’ body shapes. MetaHuman Animator – which previously required footage from stereo head-mounted cameras or iPhones – now supports footage from mono cameras like webcams. The toolset can also now generate facial animation – both lip sync and head movement – solely from audio recordings, as well as from video footage. You can find fuller descriptions of the new features in Epic Games’ blog post. Use MetaHumans in Unity or Godot games, or sell them on online marketplaces Equally significantly, Epic has changed the licensing for MetaHumans.The MetaHuman toolset is now covered by the standard Unreal Engine EULA, meaning that it can be used for free by any artist or studio with under $1 million/year in revenue. MetaHuman characters and clothing can also now be sold on online marketplaces, or used in commercial projects created with other DCC apps or game engines. The only exception is for AI: you can use MetaHumans in “workflows that incorporate artificial intelligence technology”, but not to train or enhance the AI models themselves. Studios earning more than $1 million/year from projects that use MetaHuman characters need Unreal Engine seat licenses, with currently cost $1,850/year. However, since MetaHuman characters and animations are classed as ‘non-engine products’, they can be used in games created in other engines, like Unity or Godot, without incurring the 5% cut of the revenue that Epic takes from Unreal Engine games. https://www.cgchannel.com/wp-content/uploads/2025/06/250604_MetaHumanOfficialLaunch_LicensingChanges_MetaHumanForMaya.mp4 The free MetaHuman for Maya plugin lets you edit MetaHumans with Maya’s native tools. New plugins streamline editing MetaHumans in Maya and Houdini Last but not least, Epic Games has released new free add-ons intended to streamline the process of editing MetaHumans in other DCC software.The MetaHuman for Maya plugin makes it possible to manipulate the MetaHuman mesh directly with Maya’s standard mesh-editing and sculpting tools. Users can also create MetaHuman-compatible hair grooms using Maya’s XGen toolset, and export them in Alembic format. The MetaHuman for Houdini plugin seems to be confined to grooming, with users able to create hairstyles using Houdini’s native tools, and export them in Alembic format. The plugins themselves are supplemented by MetaHuman Groom Starter Kits for Maya and Houdini, which provide readymade sample files for generating grooms. Price, licensing and system requirements MetaHuman Creator and MetaHuman Animator are integrated into Unreal Engine 5.6. The Unreal Editor is compatible with Windows 10+, macOS 14.0+ and RHEL/Rocky Linux 8+.The MetaHuman plugin for Maya is compatible with Maya 2022-2025. The MetaHuman for Houdini plugin is compatible with Houdini 20.5 with SideFX Labs installed. All of the software is free to use, including for commercial projects, if you earn under $1 million/year. You can find more information on licensing in the story above. Read an overview of the changes to the MetaHuman software on Epic Games’ blog Download the free MetaHuman for Maya and Houdini plugins and starter kits Read Epic Games’ FAQs about the changes to licensing for MetaHumans Have your say on this story by following CG Channel on Facebook, Instagram and X (formerly Twitter). As well as being able to comment on stories, followers of our social media accounts can see videos we don’t post on the site itself, including making-ofs for the latest VFX movies, animations, games cinematics and motion graphics projects.

After the Nintendo Switch, the Steam Deck might be the most impressive gaming handheld of the last decade. It brings Steam games—most of which were initially designed to run on Windows PCs—to a remarkably designed portable device. The only problem? Battery life can be rough on some games. If you're struggling to stay charged, here are some tips to help you out.When it comes to your Steam Deck's battery life, you're going to notice a lot of variability, even from one game to another. AAA games that rely on high-end GPUs will typically guzzle power. On laptops or desktops, that's usually not as much of a concern, but on the Steam Deck—when those games run at all—they can burn through the battery quickly.So, while we have plenty of tips to get the best battery life, it's important to keep in mind some games will simply burn through your power no matter what. Fortunately, SteamOS is already pretty power efficient (at least compared to other operating systems), and there are several handy tools to help.First, learn what, exactly, is draining your batteryThere are a few common culprits for battery drain in games, and it's helpful to understand them before diving into solutions. This is because what works for one game with minimal performance impact, could make another game unplayable. With that in mind, here are a few key things that drain your battery:Your hardware settings. The display on your Steam Deck is always a pretty big battery drain, and turning down the brightness can help. Wireless radios like wifi or Bluetooth are always sipping power, even if you're not using them, so you can sometimes turn these off if you don't need them.Your refresh rate and FPS. Your Steam Deck has to update the screen dozens of times every second, and for some games it might be way more than necessary. 60 to 90 frames per second might be necessary for a fast-paced game like Doom Eternal, but it's overkill for Stardew Valley.Your processor's TDP. Thermal Design Power (or TDP) is a complicated metric, but it serves as a shorthand for how much power your processor is using. On the Steam Deck, you can limit this directly, which is a blunt way of saving battery, but it can help sometimes.The most useful tool to help you diagnose your biggest battery drains is the Performance Overlay. Press the three-dot menu button while in a game and navigate to the Performance section and you'll see an option to enable this overlay. There are several levels of detail, ranging from a simple frame rate counter, to real-time power consumption and temperature readouts. The Performance tab is also where you'll find several useful features we'll discuss (under Advanced View), so it's good to make friends with this tab.Dive into your game's display settingsWhile the Steam Deck has a lot of useful features for managing battery life, you're still going to find some of your best options in your game's settings. Most games have presets to lower graphics settings with one quick toggle—like switching from Ultra to Medium—and some have even more advanced settings.This is particularly important to keep in mind if you play Steam games on multiple devices. Some games will try to sync settings between them, which can lead to your game rendering at a higher resolution or frame rate than the Steam Deck is even capable of displaying.In general, here are a few settings you should take a look at:Resolution: The Steam Deck has a 1280x800 resolution, so unless you're using an external monitor, there's no reason to set your game to a higher resolution. Most games won't let you go higher anyway, but it's worth it to double check. You can also go lower for some games, if you don't need as much detail.Frame rate: Many games offer the ability to cap how many frames the game generates, even if your display is capable of showing more. This can have a substantial impact on your battery life, especially for games that need to perform a lot of complex calculations (like graphics-heavy shooters) for every new frame.Graphical presets: If your game has a preset slider, try starting on the lowest preset and working your way up to see how the game performs. The Performance Overlay can be a huge help here, to see how much power your system is drawing on different presets. If your eye can't tell the difference, but your battery can, drop the settings.You can play around to find the right balance for you, and it will vary greatly by game. In some games, you might want more graphical detail, but fewer frames per second, while others would benefit from the exact opposite. Try a few options to see what works best.Adjust your refresh rate and FPS in tandem with the Frame Limit sliderAs mentioned above, the number of times your game updates the screen per second can be a huge factor in battery drain. This is affected by both the screen's refresh rate (how many times the display physically updates the pixel you see) and your game's frames per second (or FPS, the number of times the GPU generates a new frame per second). To complicate matters further, your refresh rate can have an effect on your input latency, meaning it's important to strike a delicate balance.To simplify this, the Steam Deck has a slider called Frame Limit that can impose a limit on how many frames your game displays and strike that balance for you. It automatically adjusts your refresh rate to be evenly divisible by the FPS limit, avoiding unnecessary (and asynchronous) refreshes, while still maintaining the highest refresh rate possible to reduce input lag.It's a workaround that's placed somewhat late in the pipeline, and it's sometimes better to adjust your game's settings directly, but it simplifies a complicated process. If you'd rather adjust your display's refresh rate directly, you can toggle Disable Frame Limit and adjust the refresh rate from 45Hz to 90Hz directly. Keep in mind, though, you might still need to adjust some game settings to avoid generating frames your display will just throw out.Put a cap on your Thermal Design Power (TDP), if you mustTweaking your game's graphics settings can adjust your power consumption with scalpel-like precision. By comparison, the TDP limit is a hammer. But even hammers have their uses. By design, the TDP slider on the Steam Deck will put a hard limit on how much power the CPU/GPU can draw from the battery. You can't get much more direct battery savings than that.The problem is that games typically, you know, need power. And even games with really fine-grain settings don't generally ask the user to decide how much electricity to draw. For some, especially graphics-heavy games, putting a hard limit on TDP can cause massive performance drops or even game crashes.Less demanding games, though, can benefit from playing with this setting. A useful rule of thumb is that if the game you're playing is already struggling to maintain a consistent frame rate, try something else before touching TDP. But for games like Stardew Valley, where you're never really concerned with frame rate, you can experiment with lowering the TDP limit to 10W or even 5W to see how well the game performs.Of course, setting a TDP limit only matters if it's below what your game was using in the first place. This is another area where the performance overlay comes in handy. You can get a sense of how much power your system is drawing during your games, and use that to gauge how low you want your TDP limit to be.Don't forget per-game battery setting profilesOn top of all these settings, you can also set game-specific profiles to change your battery settings automatically based on the title you're playing. I can't recommend this feature enough, especially if you tend to play games with very different power demands. Few things are more annoying than forgetting you set a low TDP limit for a simple game, then launching a more demanding game that strains against that limit.To use this, it's one simple toggle on the Performance tab. Enable "Use per-game profile" and the Steam Deck will automatically create a profile for every game you use. You can disable this toggle to switch back to the default, if you ever decide you prefer one consistent profile.Keep in mind the profiles only account for the Steam Deck's settings itself, not any game-specific settings. But it's still a handy tool. It can be overwhelming to keep track of all the different buttons and knobs you can fiddle with to get extra battery life, but the Steam Deck manages to balance a ton of customization options with the simplicity of straight-forward, user-friendly tools so you can game longer.

A number of Sonos speakers are on sale right now at Sonos direct and Amazon. This includes the well-regarded Era 300 smart speaker, which is on sale for $359. This particular model is one of Sonos' newest, and it has rarely gone on sale in the past. We enjoyed the Era 300 enough to give it a score of 80 in our review. It has excellent sound quality and offers a premium experience that far surpasses other products in the company's lineup, even the Era 100. This is also true when compared to rival speakers like Apple's HomePod. It's simple to set up and offers the company's proprietary Trueplay tuning system. This feature optimizes the sound of the speaker to the unique acoustics of a room by leveraging an internal microphone. It measures how sound reflects off surfaces and adjusts the EQ to match. It's pretty nifty. As for connectivity, it can pair with another Era 300 speaker for a true stereo experience. It also includes a Bluetooth receiver and line-in options. Of course, the speaker integrates with just about every streaming music service. The built-in mic also allows for voice assistant control, but only with Siri and Alexa. Google Assistant is left out of the party. This speaker goes all-in on spatial audio, and the results are mixed. Sometimes it's sublime and sometimes it's kind of eh. This is more of a dig on the technology itself. It has serious potential but is still experiencing growing pains. The only real downside of this speaker is the exorbitant asking price, which has been slightly alleviated by this sale. As previously mentioned, other Sonos products are available at a discount. This includes the Sonos Beam Gen 2 soundbar, which is 26 percent off at $369. These deals are available via Sonos itself. There's also an ongoing sale on portable speakers.  Follow @EngadgetDeals on X for the latest tech deals and buying advice.This article originally appeared on Engadget at https://www.engadget.com/deals/the-sonos-era-300-is-20-percent-off-in-this-home-speaker-sale-150857725.html?src=rss

After a universe-shaking season 4 finale on The Boys, it’s once again time for the kids of Godolkin University to step up to the plate. While Gen V’s second season won’t premiere until September, Amazon took to CCXP Mexico to give fans a look at the new season — and a little of how The Boys season 4 impacted the college-set spinoff. Here’s the official synopsis: As the rest of America adjusts to Homelander’s iron fist, back at Godolkin University, the mysterious new Dean preaches a curriculum that promises to make students more powerful than ever. Cate and Sam are celebrated heroes, while Marie, Jordan, and Emma reluctantly return to college, burdened by months of trauma and loss. But parties and classes are hard to care about with war brewing between Humans and Supes, both on and off campus. The gang learns of a secret program that goes back to the founding of Godolkin University that may have larger implications than they realize. And, somehow, Marie is a part of it. Most of the cast is back for season 2, including Jaz Sinclair as Marie Moreau, Lizze Broadway as Emma, Maddie Phillips as Cate Dunlap, London Thor and Derek Luh as Jordan Li, Asa Germann as Sam Riordan, and Sean Patrick Thomas as Polarity. Tragically, Gen V season 1 star Chance Perdomo, who played the Magneto-like Andre, died in 2023 on his way to Toronto to film season 2. The production team had previously announced that they would not recast Perdomo’s role for season 2, and it seems the writers have incorporated his departure into Polarity’s arc. New to season 2: Hamish Linklater (Midnight Mass) as Dean Ciphe, a new villain who looks… absolutely punchable. Gen V season 2 will kick off with a three-episode premiere on Sep. 17.

Chances are, you've heard of the term "large language models," or LLMs, when people are talking about generative AI. But they aren't quite synonymous with the brand-name chatbots like ChatGPT, Google Gemini, Microsoft Copilot, Meta AI and Anthropic's Claude.These AI chatbots can produce impressive results, but they don't actually understand the meaning of words the way we do. Instead, they're the interface we use to interact with large language models. These underlying technologies are trained to recognize how words are used and which words frequently appear together, so they can predict future words, sentences or paragraphs. Understanding how LLMs work is key to understanding how AI works. And as AI becomes increasingly common in our daily online experiences, that's something you ought to know.This is everything you need to know about LLMs and what they have to do with AI.What is a language model?You can think of a language model as a soothsayer for words."A language model is something that tries to predict what language looks like that humans produce," said Mark Riedl, professor in the Georgia Tech School of Interactive Computing and associate director of the Georgia Tech Machine Learning Center. "What makes something a language model is whether it can predict future words given previous words."This is the basis of autocomplete functionality when you're texting, as well as of AI chatbots.What is a large language model?A large language model contains vast amounts of words from a wide array of sources. These models are measured in what is known as "parameters."So, what's a parameter?Well, LLMs use neural networks, which are machine learning models that take an input and perform mathematical calculations to produce an output. The number of variables in these computations are parameters. A large language model can have 1 billion parameters or more."We know that they're large when they produce a full paragraph of coherent fluid text," Riedl said.How do large language models learn?LLMs learn via a core AI process called deep learning."It's a lot like when you teach a child -- you show a lot of examples," said Jason Alan Snyder, global CTO of ad agency Momentum Worldwide.In other words, you feed the LLM a library of content (what's known as training data) such as books, articles, code and social media posts to help it understand how words are used in different contexts, and even the more subtle nuances of language. The data collection and training practices of AI companies are the subject of some controversy and some lawsuits. Publishers like The New York Times, artists and other content catalog owners are alleging tech companies have used their copyrighted material without the necessary permissions.(Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed on Ziff Davis copyrights in training and operating its AI systems.)AI models digest far more than a person could ever read in their lifetime -- something on the order of trillions of tokens. Tokens help AI models break down and process text. You can think of an AI model as a reader who needs help. The model breaks down a sentence into smaller pieces, or tokens -- which are equivalent to four characters in English, or about three-quarters of a word -- so it can understand each piece and then the overall meaning.From there, the LLM can analyze how words connect and determine which words often appear together."It's like building this giant map of word relationships," Snyder said. "And then it starts to be able to do this really fun, cool thing, and it predicts what the next word is … and it compares the prediction to the actual word in the data and adjusts the internal map based on its accuracy."This prediction and adjustment happens billions of times, so the LLM is constantly refining its understanding of language and getting better at identifying patterns and predicting future words. It can even learn concepts and facts from the data to answer questions, generate creative text formats and translate languages. But they don't understand the meaning of words like we do -- all they know are the statistical relationships.LLMs also learn to improve their responses through reinforcement learning from human feedback."You get a judgment or a preference from humans on which response was better given the input that it was given," said Maarten Sap, assistant professor at the Language Technologies Institute at Carnegie Mellon University. "And then you can teach the model to improve its responses." LLMs are good at handling some tasks but not others.

Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language models (LLMs) attempt to mimic through structured approaches such as chain-of-thought (CoT) prompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem. A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This causes the “overthinking” problem, where models generate unnecessarily verbose explanations for simpler tasks. Not only does this waste resources, but it also degrades accuracy, as excessive reasoning can introduce irrelevant information. Approaches such as prompt-guided generation or token budget estimation have attempted to mitigate this issue. Still, these methods are limited by their dependence on predefined assumptions, which are not always reliable for diverse tasks. Attempts to address these issues include methods like GRPO (Group Relative Policy Optimization), length-penalty mechanisms, and rule-based prompt controls. While GRPO enables models to learn different reasoning strategies by rewarding correct answers, it leads to a “format collapse,” where models increasingly rely on Long CoT, crowding out more efficient formats, such as Short CoT or Direct Answer. Length-penalty techniques, such as those applied in methods like THINKPRUNE, control output length during training or inference, but often at the cost of reduced accuracy, especially in complex problem-solving tasks. These solutions struggle to achieve a consistent trade-off between reasoning effectiveness and efficiency, highlighting the need for an adaptive approach. A team of researchers from Fudan University and Ohio State University introduced the Adaptive Reasoning Model (ARM), which dynamically adjusts reasoning formats based on task difficulty. ARM supports four distinct reasoning styles: Direct Answer for simple tasks, Short CoT for concise reasoning, Code for structured problem-solving, and Long CoT for deep multi-step reasoning. It operates in an Adaptive Mode by default, automatically selecting the appropriate format, and also provides Instruction-Guided and Consensus-Guided Modes for explicit control or aggregation across formats. The key innovation lies in its training process, which utilizes Ada-GRPO, an extension of GRPO that introduces a format diversity reward mechanism. This prevents the dominance of Long CoT and ensures that ARM continues to explore and use simpler reasoning formats when appropriate. The ARM methodology is built on a two-stage framework. First, the model undergoes Supervised Fine-Tuning (SFT) with 10.8K questions, each annotated across four reasoning formats, sourced from datasets like AQuA-Rat and generated with tools such as GPT-4o and DeepSeek-R1. This stage teaches the model the structure of each reasoning format but does not instill adaptiveness. The second stage applies Ada-GRPO, where the model receives scaled rewards for using less frequent formats, such as Direct Answer or Short CoT. A decaying factor ensures that this reward gradually shifts back to accuracy as training progresses, preventing long-term bias toward inefficient exploration. This structure enables ARM to avoid format collapse and dynamically match reasoning strategies to task difficulty, achieving a balance of efficiency and performance. ARM demonstrated impressive results across various benchmarks, including commonsense, mathematical, and symbolic reasoning tasks. It reduced token usage by an average of 30%, with reductions as high as 70% for simpler tasks, compared to models relying solely on Long CoT. ARM achieved a 2x training speedup over GRPO-based models, accelerating model development without sacrificing accuracy. For example, ARM-7B achieved 75.9% accuracy on the challenging AIME’25 task while using 32.5% fewer tokens. ARM-14B achieved 85.6% accuracy on OpenBookQA and 86.4% accuracy on the MATH dataset, with a token usage reduction of over 30% compared to Qwen2.5SFT+GRPO models. These numbers demonstrate ARM’s ability to maintain competitive performance while delivering significant efficiency gains. Overall, the Adaptive Reasoning Model addresses the persistent inefficiency of reasoning models by enabling the adaptive selection of reasoning formats based on task difficulty. The introduction of Ada-GRPO and the multi-format training framework ensures that models no longer waste resources on overthinking. Instead, ARM provides a flexible and practical solution for balancing accuracy and computational cost in reasoning tasks, making it a promising approach for scalable and efficient large language models. Check out the Paper, Models on Hugging Face and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural NetworksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRIT: A Method for Teaching MLLMs to Reason with Images by Interleaving Text and Visual Grounding