241 citations · 983 across the 67 of their papers we have counts for
11 papers · 1 filter
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…
RefineBench: Evaluating Refinement Capability of Language Models via Checklists
Young-Jun Lee, Seungone Kim, Byung-Kwan Lee +6
Can language models (LMs) self-refine their own responses? This question is increasingly relevant as a wide range of real-world user interactions involve refinement requests. Howev…
Training Proactive and Personalized LLM Agents
Weiwei Sun, Xuhui Zhou, Weihua Du +5
Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents as collaborators that communica…
Agentic-R1: Distilled Dual-Strategy Reasoning
Weihua Du, Pranjal Aggarwal, Sean Welleck +1
Current long chain-of-thought (long-CoT) models excel at mathematical reasoning but rely on slow and error-prone natural language traces. Tool-augmented agents address arithmetic v…
OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
Pranjal Aggarwal, Seungone Kim, Jack Lanchantin +4
Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder…
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…