8 papers
Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
Maxime Heuillet, Yufei Cui, Boxing Chen +2
Advanced reasoning in LLMs on challenging domains like mathematical reasoning can be tackled using verifiable rewards based reinforced fine-tuning (ReFT). In standard ReFT framewor…
Beyond Mode-Seeking RL: Trajectory-Balance Post-Training for Diffusion Language Models
Saba Ahmadi, Prasanna Parthasarathi, Yufei Cui
Diffusion language models are a promising alternative to autoregressive models, yet post-training methods for them largely adapt reward-maximizing objectives. We identify a central…
InfMem: Learning System-2 Memory Control for Long-Context Agent
Xinyu Wang, Mingze Li, Peng Lu +6
Reasoning over ultra-long documents requires synthesizing sparse evidence scattered across distant segments under strict memory constraints. While streaming agents enable scalable…
Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models
Michael R. Metel, Yufei Cui, Boxing Chen +1
Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed…
Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination
Jerry Huang, Prasanna Parthasarathi, Mehdi Rezagholizadeh +2
The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some of this is also owed to the risks and co…
GRPO-: Credit Assignment improves LLM Reasoning
Prasanna Parthasarathi, Mathieu Reymond, Boxing Chen +2
Large language models (LLMs) are increasingly deployed for tasks requiring complex reasoning, prompting significant interest in improving their reasoning abilities through post-tra…