5 citations · 20 across the 16 of their papers we have counts for
4 papers · 1 filter
Multi-Turn Puzzles: Evaluating Interactive Reasoning and Strategic Dialogue in LLMs
Kartikeya Badola, Jonathan Simon, Arian Hosseini +7
Large language models (LLMs) excel at solving problems with clear and complete statements, but often struggle with nuanced environments or interactive tasks which are common in mos…
CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
Congmin Zheng, Jiachen Zhu, Jianghao Lin +6
Process Reward Models (PRMs) play a central role in evaluating and guiding multi-step reasoning in large language models (LLMs), especially for mathematical problem solving. Howeve…
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers
Kusha Sareen, Morgane M Moss, Alessandro Sordoni +2
Prevalent reinforcement learning~(RL) methods for fine-tuning LLM reasoners, such as GRPO or Leave-one-out PPO, abandon the learned value function in favor of empirically estimated…
When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning
Nishad Singhi, Hritik Bansal, Arian Hosseini +4
Scaling test-time compute has emerged as a key strategy for enhancing the reasoning capabilities of large language models (LLMs), particularly in tasks like mathematical problem-so…