VPR converts symbolic, constraint, or posterior oracles into dense turn-level rewards for RL, improving credit assignment in agentic reasoning and transferring to general benchmarks.
SWE-bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations
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LLM agents improve adaptability by first using an interaction budget for systematic exploration measured via Exploration Checkpoint Coverage before executing tasks.
citing papers explorer
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Verifiable Process Rewards for Agentic Reasoning
VPR converts symbolic, constraint, or posterior oracles into dense turn-level rewards for RL, improving credit assignment in agentic reasoning and transferring to general benchmarks.
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Look Before You Leap: Autonomous Exploration for LLM Agents
LLM agents improve adaptability by first using an interaction budget for systematic exploration measured via Exploration Checkpoint Coverage before executing tasks.