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Open-weight AI models are getting seriously capable. IQuest-Q1 just dropped, and it’s built for more than generating code. → ~320B total parameters, ~15B active per token → Strong results on NL2Repo and CyberGym → Competitive on Terminal-Bench 2.1 and Agents’ Last Exam → Can build interactive 3D apps and...

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Atul Kumar

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79,434 просмотров • 7 дней назад •via X (Twitter)

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Комментарии: 13

Фото профиля Manish Kumar Shah
Manish Kumar Shah7 дней назад

Models that can diagnose, plan, execute, and verify could transform how technical teams operate.

Фото профиля Rishabh
Rishabh7 дней назад

If these capabilities scale, AI-assisted R&D could look radically different very soon.

Фото профиля IamAlam
IamAlam7 дней назад

The ability to inspect, modify, test, and verify feels like a major leap.

Фото профиля RAVI KUMAR SAHU
RAVI KUMAR SAHU7 дней назад

Building interactive 3D experiences from natural language shows how far coding agents have come.

Фото профиля Satendra Tiwari
Satendra Tiwari7 дней назад

Great share

Фото профиля Tanvir Anjum
Tanvir Anjum7 дней назад

Open-weight models are moving remarkably fast.

Фото профиля mamta Devi
mamta Devi7 дней назад

Great share

Фото профиля The AI Colony
The AI Colony7 дней назад

Long-horizon execution is where AI starts feeling less like autocomplete and more like collaboration.

Фото профиля Pradeep Pandey
Pradeep Pandey7 дней назад

This is the kind of capability jump that makes open-weight AI especially compelling.

Фото профиля Aryan Rakib
Aryan Rakib7 дней назад

The R&D assistance angle might ultimately be more important than raw benchmark performance.

Фото профиля Parul Gautam
Parul Gautam7 дней назад

Open-weight models are moving fast, and agentic capabilities are becoming genuinely impressive.

Фото профиля kamran Hassan
kamran Hassan7 дней назад

Autonomous debugging and iterative verification could become foundational features of future coding agents.

Фото профиля Patrick's AIBuzzNews
Patrick's AIBuzzNews7 дней назад

This looks like a powerful model!

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Wait… IQuest-Q1 actually built this from a written brief. I asked it to build Velora, an interactive SaaS analytics dashboard The result? A complete working dashboard I could click through, filter and explore. This is what makes IQuest-Q1 interesting. It’s built for more than standard code generation. IQuest-Q1 is a large, code-focused model from IQuest Research, and its capabilities go beyond simply writing code. It is being evaluated across: → Game & application development → 3D spatial generation → Code diagnosis & repair → Multimodal tasks with tools And that matters because real-world software development is rarely just about writing code. You need to build, debug, reason, use tools, and work across different environments. That’s why the Velora demo is interesting: I gave it a product brief, and it turned that idea into an interactive web experience. The goal is not just to generate code. It’s to handle more of the workflow from idea → implementation → working product. #coding #LLM
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Wait… IQuest-Q1 actually built this from a written brief. I asked it to build Velora, an interactive SaaS analytics dashboard The result? A complete working dashboard I could click through, filter and explore. This is what makes IQuest-Q1 interesting. It’s built for more than standard code generation. IQuest-Q1 is a large, code-focused model from IQuest Research, and its capabilities go beyond simply writing code. It is being evaluated across: → Game & application development → 3D spatial generation → Code diagnosis & repair → Multimodal tasks with tools And that matters because real-world software development is rarely just about writing code. You need to build, debug, reason, use tools, and work across different environments. That’s why the Velora demo is interesting: I gave it a product brief, and it turned that idea into an interactive web experience. The goal is not just to generate code. It’s to handle more of the workflow from idea → implementation → working product. #coding #LLM

Atal

41,419 просмотров • 5 дней назад

From a written idea to a 3D racing game you can open and play. 🏎️ In the IQuest‑Q1 demo, the model generates the track, the car, the controls, and the interface based on a natural language description. The result demonstrates something more interesting than just a pretty screen: the game elements must work together as the car moves along the circuit. IQuest‑Q1 is an open-weights model designed for programming and multi-step tasks. This demo caught my eye because of the number of components it brings together into a single application. My next test would be to ask for specific changes to the track and the driving mechanics to see how it responds during iteration. What would you add to the game?
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From a written idea to a 3D racing game you can open and play. 🏎️ In the IQuest‑Q1 demo, the model generates the track, the car, the controls, and the interface based on a natural language description. The result demonstrates something more interesting than just a pretty screen: the game elements must work together as the car moves along the circuit. IQuest‑Q1 is an open-weights model designed for programming and multi-step tasks. This demo caught my eye because of the number of components it brings together into a single application. My next test would be to ask for specific changes to the track and the driving mechanics to see how it responds during iteration. What would you add to the game?

Alexander Inspira IA

50,997 просмотров • 7 дней назад

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I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

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Open-ended coding training data may no longer be the bottleneck: AI can scale open-ended tasks—and even outperform human-expert curation. FrontierCS team is releasing FrontierSmith: a system for synthesizing open-ended coding problems at scale. Starting from closed-ended coding tasks, FrontierSmith mutates, filters, and builds runnable optimization environments for long-horizon coding agents. In our experiments, FrontierSmith data trains stronger models than human-curated open-ended data on FrontierCS and ALE-bench. Blog: Paper: Code: Model:
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Open-ended coding training data may no longer be the bottleneck: AI can scale open-ended tasks—and even outperform human-expert curation. FrontierCS team is releasing FrontierSmith: a system for synthesizing open-ended coding problems at scale. Starting from closed-ended coding tasks, FrontierSmith mutates, filters, and builds runnable optimization environments for long-horizon coding agents. In our experiments, FrontierSmith data trains stronger models than human-curated open-ended data on FrontierCS and ALE-bench. Blog: Paper: Code: Model:

Qiuyang Mang

121,283 просмотров • 4 месяцев назад

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RAVI KUMAR SAHU

63,568 просмотров • 1 месяц назад

Open models are how developers build AI they can own. Not just weights. Model families, post-training recipes, evaluation tools, and supporting data give teams the ability to inspect, adapt, and deploy AI for real applications. At GTC Berlin, Bryan Catanzaro, VP of Applied Deep Learning Research at NVIDIA, will show developers what that looks like in practice. 🎙️ Building Open Models That Developers Can Inspect, Adapt, and Deploy: 📍 #NVIDIAGTC Berlin | October 20–22, 2026 🔗 Register with code GTC-NVP8A3H2 for 25% off:
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Open models are how developers build AI they can own. Not just weights. Model families, post-training recipes, evaluation tools, and supporting data give teams the ability to inspect, adapt, and deploy AI for real applications. At GTC Berlin, Bryan Catanzaro, VP of Applied Deep Learning Research at NVIDIA, will show developers what that looks like in practice. 🎙️ Building Open Models That Developers Can Inspect, Adapt, and Deploy: 📍 #NVIDIAGTC Berlin | October 20–22, 2026 🔗 Register with code GTC-NVP8A3H2 for 25% off:

NVIDIA GTC

14,555 просмотров • 20 дней назад

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Michael Rabinovich

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Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 просмотров • 2 месяцев назад

This first-person shooter was built from a single prompt, using 404-generated 3D assets. On the latest Novelty Search, 404 explains how SN17 is moving from Titan, its open image-to-3D model built with miners, toward 3D assets generated as code that AI agents can edit and use directly in games. That work is part of a broader direction for 404: building world models where AI agents can learn and act. Hosted by const Full episode in the first comment
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This first-person shooter was built from a single prompt, using 404-generated 3D assets. On the latest Novelty Search, 404 explains how SN17 is moving from Titan, its open image-to-3D model built with miners, toward 3D assets generated as code that AI agents can edit and use directly in games. That work is part of a broader direction for 404: building world models where AI agents can learn and act. Hosted by const Full episode in the first comment

Openτensor Foundaτion

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The new Codex is another jump in what agents will look like for knowledge workers. Agents that can code, work with tools, and use computers, can begin to execute long running tasks in the background for all areas of work. This can mean drafting reports, setting up data rooms for a merger, reviewing contracts, helping onboard clients, generating marketing assets, processing invoices, and more. With the Box plugin inside of the new Codex, you can begin to automate almost any kind of work with enterprise content. And importantly, being able to work across multiple apps is a huge point of leverage because we can now far more easily connect our tools together.

Aaron Levie

71,589 просмотров • 5 месяцев назад

Nex-N2.5 feels less like “another model launch” and more like a push toward agents that can actually finish real work. The part that stands out to me is Nex-N2.5 Pro: a 397B multimodal model built for Vision, Computer Use, and long-horizon interaction. Instead of only understanding a screen, it can keep reading changing UI states and continue operating software step by step. That means workflows like: → moving from an idea to a Blender scene → operating CAD and other professional tools → navigating browsers and software end to end → writing code, running it, checking the interface, fixing issues, and verifying again The brief even shows examples spanning games, Blender, FreeCAD, LabPlot, GeoGebra, and other real desktop workflows. Nex-N2.5 comes in Mini, Pro, and Max, with Mini and Pro focused on multimodal agent capabilities and Max using a 1.6T text-only MoE base for more complex agentic reasoning and coding. The direction is clear: AI agents are moving from “tell me what to do” toward “open the tool and do it.” Nex-N2.5 Mini and Pro are available on OpenRouter free for a limited time:
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Nex-N2.5 feels less like “another model launch” and more like a push toward agents that can actually finish real work. The part that stands out to me is Nex-N2.5 Pro: a 397B multimodal model built for Vision, Computer Use, and long-horizon interaction. Instead of only understanding a screen, it can keep reading changing UI states and continue operating software step by step. That means workflows like: → moving from an idea to a Blender scene → operating CAD and other professional tools → navigating browsers and software end to end → writing code, running it, checking the interface, fixing issues, and verifying again The brief even shows examples spanning games, Blender, FreeCAD, LabPlot, GeoGebra, and other real desktop workflows. Nex-N2.5 comes in Mini, Pro, and Max, with Mini and Pro focused on multimodal agent capabilities and Max using a 1.6T text-only MoE base for more complex agentic reasoning and coding. The direction is clear: AI agents are moving from “tell me what to do” toward “open the tool and do it.” Nex-N2.5 Mini and Pro are available on OpenRouter free for a limited time:

Alamin

66,447 просмотров • 23 дней назад

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.
1:05

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

243,123 просмотров • 1 месяц назад

Chamath Palihapitiya believes AGI may already exist inside leading AI labs and the bigger story is that advanced intelligence is becoming cheaper and more widely available (Save this). Chamath Palihapitiya argues that the public may be focused too much on benchmark rankings, while frontier labs are already developing models capable of complex reasoning, coding, research, and tool use. The main question is how quickly companies will release these systems and how much access they will provide. AGI has not been officially confirmed and strong benchmark results do not necessarily prove that a model can perform every intellectual task like a human. However, AI capabilities are improving quickly, while the cost of running advanced models continues to fall. That combination is important because cheaper AI can be used by more businesses for customer service, software development, research, marketing, financial analysis, and automation. Competition is also accelerating among OpenAI, Anthropic, Google, xAI, Meta, and open source developers because as more companies release capable models, users gain more choices and prices continue to decline. This creates a powerful cycle in which better models attract more users, more usage generates more revenue and data, and lower prices encourage companies to apply AI to additional tasks. The biggest challenge is moving from impressive demonstrations to measurable business results. Companies still need to redesign workflows, train employees, protect sensitive information, and prove that AI spending is producing a real return on investment. AI agents could create the next major increase in demand because they can plan tasks, use tools, check their work, retry failed actions and operate for long periods without constant human supervision. Even if each AI task becomes cheaper, total usage could grow much faster as businesses use models across more departments and this could increase demand for GPUs, high bandwidth memory, networking equipment, electricity, cooling systems, and data centers.
2:45

Chamath Palihapitiya believes AGI may already exist inside leading AI labs and the bigger story is that advanced intelligence is becoming cheaper and more widely available (Save this). Chamath Palihapitiya argues that the public may be focused too much on benchmark rankings, while frontier labs are already developing models capable of complex reasoning, coding, research, and tool use. The main question is how quickly companies will release these systems and how much access they will provide. AGI has not been officially confirmed and strong benchmark results do not necessarily prove that a model can perform every intellectual task like a human. However, AI capabilities are improving quickly, while the cost of running advanced models continues to fall. That combination is important because cheaper AI can be used by more businesses for customer service, software development, research, marketing, financial analysis, and automation. Competition is also accelerating among OpenAI, Anthropic, Google, xAI, Meta, and open source developers because as more companies release capable models, users gain more choices and prices continue to decline. This creates a powerful cycle in which better models attract more users, more usage generates more revenue and data, and lower prices encourage companies to apply AI to additional tasks. The biggest challenge is moving from impressive demonstrations to measurable business results. Companies still need to redesign workflows, train employees, protect sensitive information, and prove that AI spending is producing a real return on investment. AI agents could create the next major increase in demand because they can plan tasks, use tools, check their work, retry failed actions and operate for long periods without constant human supervision. Even if each AI task becomes cheaper, total usage could grow much faster as businesses use models across more departments and this could increase demand for GPUs, high bandwidth memory, networking equipment, electricity, cooling systems, and data centers.

Milk Road AI

13,501 просмотров • 1 месяц назад

Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.
0:44

Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.

Arena.ai

543,501 просмотров • 4 месяцев назад

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
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New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 просмотров • 1 год назад

4 years ago we were on the brink of AI becoming proprietary and centralized, when OpenAI kept GPT3 closed and VCs started dumping money on researchers. From fully open science, to fully closed, in a matter of months. It was scary, and 1,000+ leading researchers and scientists banded together to show the world that it was possible to do the same work in the open, and build an ecosystem that benefits everyone. That was the BigScience Research Workshop BLOOM project, and it put us back on track to open science, starting with forward-thinking organizations like Meta releasing OPT. Look at us now. Open models have not only caught up, they're state of the art now. Not just LLMs, but models for document AI, speech to text, text to speech, generating images and more. We're closing in on 2 million open weight models on Hugging Face. Thanks for the reminder Thomas Wolf .
2:20

4 years ago we were on the brink of AI becoming proprietary and centralized, when OpenAI kept GPT3 closed and VCs started dumping money on researchers. From fully open science, to fully closed, in a matter of months. It was scary, and 1,000+ leading researchers and scientists banded together to show the world that it was possible to do the same work in the open, and build an ecosystem that benefits everyone. That was the BigScience Research Workshop BLOOM project, and it put us back on track to open science, starting with forward-thinking organizations like Meta releasing OPT. Look at us now. Open models have not only caught up, they're state of the art now. Not just LLMs, but models for document AI, speech to text, text to speech, generating images and more. We're closing in on 2 million open weight models on Hugging Face. Thanks for the reminder Thomas Wolf .

Jeff Boudier 🤗

21,956 просмотров • 1 год назад

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 просмотров • 6 месяцев назад

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.
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We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

Rerun

29,684 просмотров • 7 месяцев назад

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
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Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,157 просмотров • 1 год назад