Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning (2025.emnlp-industry)
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| Challenge: | Currently, the dominant end-to-end reinforcement learning paradigm for agents in Large Language Models (LLMs) employs multi-objective optimization that jointly trains both planning and answer summarization capabilities. |
| Approach: | They propose a framework that decouples the training process to enable a focused, single-objective optimization of the planning module. |
| Outcome: | The proposed framework achieves an 8%–12% improvement in planning performance compared to end-to-end baselines. |
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