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• ProCeedRL incorporates reflection-based demonstrations to guide agents in stopping the accumulation of errors.method 90%
• Standard exploration strategies in RL for multi-turn agentic tasks are ineffective due to a cumulative feedback loop of errors caused by suboptimal actions and noisy environmental feedback.technical 90%
• ProCeedRL shifts exploration from passive selection to active intervention by using a process-level critic to monitor interactions in real time.method 90%
• ProCeedRL significantly exceeds the model's saturated exploration performance, demonstrating substantial exploratory benefits.result 85%
• ProCeedRL significantly improves exploration efficiency by learning from exploratory demonstrations and on-policy samples.result 85%
• ProCeedRL achieves superior performance on complex deep search and embodied tasks.result 85%
• Applying RL to multi-turn agentic tasks is challenging due to the long-horizon nature of interactions and the stochasticity of environmental feedback.technical 90%

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