6 papers
Multi-Agent Computer Use
Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried
Computer use agents (CUAs) today are primarily deployed as single serial agents. This setup is suboptimal for complex long-horizon tasks that benefit from task decomposition, paral…
Scaling Test-Time Compute for Agentic Coding
Joongwon Kim, Wannan Yang, Kelvin Niu +13
Test-time scaling has become a powerful way to improve large language models. However, existing methods are best suited to short, bounded outputs that can be directly compared, ran…
Reasoning with Latent Tokens in Diffusion Language Models
Andre He, Sean Welleck, Daniel Fried
Discrete diffusion models have recently become competitive with autoregressive models for language modeling, even outperforming them on reasoning tasks requiring planning and globa…
Propose, Solve, Verify: Self-Play Through Formal Verification
Alex Wilf, Pranjal Aggarwal, Bryan Parno +4
Training models through self-play alone (without any human data) has been a longstanding goal in AI, but its effectiveness for training large language models remains unclear, parti…
From Reproduction to Replication: Evaluating Research Agents with Progressive Code Masking
Gyeongwon James Kim, Alex Wilf, Louis-Philippe Morency +1
Recent progress in autonomous code generation has fueled excitement around AI agents capable of accelerating scientific discovery by running experiments. However, there is currentl…
Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening
Andre He, Daniel Fried, Sean Welleck
Reinforcement learning is emerging as a primary driver for improving language model reasoning capabilities. A fundamental question is whether current reinforcement learning algorit…