5 papers · 1 filter
A Safe Action Is Not Enough: Feasible-Future Decoding for Vision-Language-Action Policies
Tu Nguyen, Matthieu Zimmer, Vu Anh Vu +4
A safe action is not necessarily a viable one. A frozen vision-language-action (VLA) policy can favor a locally admissible move that leaves no policy-supported route to safe task c…
The Model Knows, the Decoder Finds: Future Value Guided Particle Power Sampling
Tu Nguyen, Matthieu Zimmer, Rasul Tutunov +2
A recurring pattern in "reasoning without training" is that base LLMs already assign non-trivial probability mass to correct multi-step solutions; the bottleneck is locating these…
Risk-Controlled Lean-as-Judge for Natural-Language Mathematical Reasoning
Pauline Bourigault, Xiaotong Ji, Matthieu Zimmer +2
Lean is increasingly used to judge natural-language mathematical answers, but its signal is partial: many answers never formalize, and a failed proof may reflect an ill-typed state…
Tree-OPO: Off-policy Monte Carlo Tree-Guided Advantage Optimization for Multistep Reasoning
Bingning Huang, Tu Nguyen, Matthieu Zimmer
Recent advances in reasoning with large language models (LLMs) have shown the effectiveness of Monte Carlo Tree Search (MCTS) for generating high quality intermediate trajectories,…
Bourbaki: Self-Generated and Goal-Conditioned MDPs for Theorem Proving
Matthieu Zimmer, Xiaotong Ji, Rasul Tutunov +3
Reasoning remains a challenging task for large language models (LLMs), especially within the logically constrained environment of automated theorem proving (ATP), due to sparse rew…