ATOD: Annealed Turn-aware On-policy Distillation for Multi-turn Autonomous Agents
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 improvement toward a higher reward-defined ceiling, but sparse and delayed feedback makes early-stage learning much less efficient than OPD. Experiments on ALFWorld, WebShop, and Search-QA show that ATOD consistently outperforms competing post-training baselines: across the three student sizes, ATOD improves average success rate by 3.03 points over OPD and 23.62 points over GRPO, while surpassing the corresponding teacher models by 2.16 points.
- ▪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 improvement toward a higher reward-defined ceiling, but sparse and delayed feedback makes early-stage learning much less efficient than OPD.
- ▪Experiments on ALFWorld, WebShop, and Search-QA show that ATOD consistently outperforms competing post-training baselines: across the three student sizes, ATOD improves average success rate by 3.03 points over OPD and 23.62 points over GRPO
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.27814 |
| Publication time | Mon, 29 Jun 2026 00:00:00 -0400 |
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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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