In reinforcement learning for multi-turn agents, a sparse outcome reward makes credit assignment across intermediate tool calls difficult. On-policy self-distillation eases credit assignment by having the model teach itself with dense supervision: the model with privileged information (the teacher) scores each token generated by the model without it (the student). However, it remains unclear what forms of feedback are most effective in multi-turn agentic settings. We find that the natural multi-turn variant fails: when the teacher is given a complete successful demonstration at every turn, training makes the agent worse than the untrained model. The teacher ends up rewarding the student for copying the demonstration, even when a copied action is not right in the student's context. We propose HINT, in which a reflector, by default the policy itself, reads the completed trajectory and writes short turn-level feedback. The teacher then sees what went wrong at each turn, not the actions that followed. On Tau-Bench and WebShop, HINT matches GRPO with one-eighth as many rollouts.
Zhenduo Shang, Xiyao Liu, Bohan Li, Xudong Wang, Teng Ren, Lianqing Liu, Zhi Han
原文摘要
Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization. The existing methods predominantly focus on imitation from in-domain tasks and consequently struggle with generalization to unseen tasks. To bridge this generalization gap, we propose the \textbf{D}ynamics-\textbf{A}ware \textbf{M}eta-\textbf{I}mitation (DAMI) framework. By integrating meta-learning to construct a shared skill space, DAMI equips agents for rapid adaptation to novel tasks. We introduce the Visual-Motor Trajectory (VMT) module to capture complex spatio-temporal dynamics within the task latent space. Furthermore, we propose the Unpaired Unified Task (U2T) block to fuse unstructured multimodal observations. To coordinate these representations, we integrate a Task-Conditioned Feature Modulation (TCFM) mechanism customized for modulating low-level 3D features. By capturing intrinsic dynamics from a random complete reference demonstration, our framework learns the underlying task logic rather than memorizing static cues, ensuring effective generalization. Extensive experiments in both simulation and real-world settings demonstrate that our approach outperforms state-of-the-art baselines regarding direct inference on seen tasks and adaptation to unseen tasks via few-shot fine-tuning.