11 papers
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…
The -Combinator for LLMs: Solving Long-Context Rot with -Calculus
Amartya Roy, Rasul Tutunov, Xiaotong Ji +2
LLMs are increasingly used as general-purpose reasoners, but long inputs remain bottlenecked by a fixed context window. Recursive Language Models (RLMs) address this by externalisi…
Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers
Xiaotong Ji, Rasul Tutunov, Matthieu Zimmer +1
Decoding sits between a language model and everything we do with it, yet it is still treated as a heuristic knob-tuning exercise. We argue decoding should be understood as a princi…
Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
Shyam Sundhar Ramesh, Xiaotong Ji, Matthieu Zimmer +5
RL-based post-training with GRPO is widely used to improve large language models on individual reasoning tasks. However, real-world deployment requires reliable performance across…
Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening
Xiaotong Ji, Rasul Tutunov, Matthieu Zimmer +1
Reinforcement learning (RL) post-training is a dominant approach for improving the reasoning performance of large language models (LLMs), yet growing evidence suggests that its gai…