ATOD: Annealed Turn-aware On-policy Distillation for Multi-turn Autonomous Agents
arXiv:2606.27814v1 Announce Type: new Abstract: Training small language-model agents for long-horizon interactive tasks requires both fast imitation and reward-driven improvement. On-policy distillation (OPD) provides dense teacher guidance and typically improves rapidly in the early stage, but its gains saturate once the student approaches the teacher, limiting the final performance ceiling. Reinforcement learning (RL) directly optimizes environment rewards and encourages exploratory improvemen
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Computer Science > Artificial Intelligence arXiv:2606.27814 (cs) [Submitted on 26 Jun 2026] Title:ATOD: Annealed Turn-aware On-policy Distillation for Multi-turn Autonomous Agents Authors:Qitai Tan, Zefang Zong, Yang Li, Peng Chen View a PDF of the paper titled ATOD: Annealed Turn-aware On-policy Distillation for Multi-turn Autonomous Agents, by Qitai Tan and 3 other authors View PDF HTML (experimental) Abstract:Training small language-model agents for long-horizon interactive tasks requires both fast imitation and reward-driven improvement. On-policy distillation (OPD) provides dense teacher guidance and typically improves rapidly in the early stage, but its gains saturate once the student approaches the teacher, limiting the final performance ceiling.
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