mlsanityData to 7 Oct 2026

When Privileged Guidance Misaligns: State-Matched Routing and Contextualized Self-Distillation for Multi-Turn Agents

arXiv 2608.05219 ↗First posted 5 Aug 2026cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Privileged on-policy distillation provides dense supervision for multi-turn agents by allowing a synchronized teacher to re-score the student's response at every turn with access to training-only references, such as successful trajectories. In interactive environments, however, the student's preceding actions continually change the execution state. As the student takes different actions or completes subgoals in a different order, its rollout may reach states not covered by the reference, making the reference an unreliable source of guidance for the state actually reached. Applying privileged distillation indiscriminately therefore creates state--reference mismatch. This mismatch motivates a central objective: providing privileged reference guidance that remains compatible with the student's current execution state. We introduce State-Matched Routing and Contextualized Self-Distillation (SMRC-SD), which explicitly determines when and how a privileged trajectory should guide an on-policy student. At each turn, SMRC-SD verifies whether the student's current execution state matches a supported state along the reference trajectory. Distillation is applied only at matched states, filtering out turns for which the reference lacks locally compatible guidance. For each matched state, SMRC-SD further constructs state-conditioned teacher context from the successful trajectory, grounding supervision in the state actually reached. Across ALFWorld and WebShop, SMRC-SD consistently outperforms unconditional successful full-path distillation. With Qwen3-1.7B, it improves task success from 0.746 to 0.865 on ALFWorld and from 0.574 to 0.693 on WebShop. Controlled routing and context ablations support both selecting locally supported turns and constructing state-compatible teacher context as contributors to these gains. Code is available at https://github.com/liujunzhuo/SMRC-SD.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 5 Aug 2026.

Builds on

18 of 27 references.

References · 18

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  5. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
  6. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
  7. 2016Sequence-Level Knowledge Distillationcited by 761
  8. 2018Born Again Neural Networkscited by 539
  9. 2026TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agentscited by 38
  10. 2025ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflectioncited by 30
  11. 2024StateAct: Enhancing LLM Base Agents via Self-prompting and State-trackingcited by 21
  12. 2026TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Trainingcited by 14
6 more
  1. 2026Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidancecited by 12
  2. 2026Multi-Turn On-Policy Distillation with Prefix Replaycited by 12
  3. 2026SAGE-OPD: Selective Agent-Guided Intervention for Multi-Turn On-Policy Distillationcited by 11
  4. 2026What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agentscited by 9
  5. 2026HINT-SD: Targeted Hindsight Self-Distillation for Long-Horizon Agentscited by 7
  6. 2026From History to State: Constant-Context Skill Learning for LLM Agentscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.