Papers by Sizhe Tang
Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization (2026.findings-acl)
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| Challenge: | Existing approaches to reinforcement learning for Large Language Models treat trajectories as independent chains and ignore critical steps that may disproportionally impact reasoning outcome. |
| Approach: | They propose a framework that recovers latent correlated reward structure across seemingly independent trajectories by identifying and merging functionally similar steps/nodes. |
| Outcome: | The proposed framework recovers latent correlated reward structure across seemingly independent trajectories. |