Papers by Jingyue Gao
ProCeedRL: Process Critic with Explorative Demonstration Reinforcement Learning for LLM Agentic Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit exceptional reasoning capabilities, driven by Reinforcement Learning with Verifiable Rewards (RLVR). |
| Approach: | They propose a method that uses a process-level critic to monitor interactions in real time, incorporating reflection-based demonstrations to guide agents in stopping the accumulation of errors. |
| Outcome: | The proposed approach exceeds the model’s saturated exploration performance and achieves superior performance on complex deep search and embodied tasks. |