I am surveying papers that discuss and explain the failure modes of on-policy distillation and its variants. Will be sharing a full post soon.

Here are the papers I am focusing on:

The Many Faces of On-Policy Distillation: Pitfalls, Mechanisms, and Fixes https://arxiv.org/abs/2605.11182

TRD: Trajectory-Refined Distillation https://arxiv.org/abs/2606.08432

Rethinking On-Policy Distillation of Large Language Models https://arxiv.org/abs/2604.13016

Diagnosing and Mitigating Thinking Collapse in On-Policy Self-Distillation https://arxiv.org/abs/2607.10805

Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes https://arxiv.org/abs/2603.25562

The Many Faces of On-Policy Distillation: Pitfalls, Mechanisms, and Fixes

Source: https://arxiv.org/abs/2605.11182 View PDF

Abstract:On-policy distillation (OPD) and on-policy self-distillation (OPSD) have emerged as promising post-training methods for large language models, offering dense token-level supervision on trajectories sampled from the model’s own policy. However, existing results on their effectiveness remain mixed: while OP(S)D has shown promise in system prompt and knowledge internalization, recent studies also report instability and degradation. In this work, we present a comprehensive empirical study of when OPD and OPSD work, when they fail, and why. We find that OPD on mathematical reasoning is highly sensitive to teacher choice and loss formulation, whereas OPSD fails in our tested settings due to test-time absence of instance-specific privileged information (PI). In contrast, OPSD is effective when PI represents a shared latent rule, such as a system prompt or alignment preference. We identify three failure mechanisms: (1) distribution mismatch between teacher and student caused by conditioning on student-generated prefixes, (2) optimization instability from biased TopK reverse-KL gradients, and (3) an OPSD-specific limitation where the student learns a PI-free policy that aggregates PI-conditioned teachers, which is insufficient when PI is instance-specific. We further show that stop-gradient TopK objectives, RLVR-adapted teachers, and SFT-stabilized students mitigate these failures.

Submission history

From: Siqi Zhu [view email] **[v1]**Mon, 11 May 2026 19:44:59 UTC (7,604 KB) **[v2]**Sun, 24 May 2026 01:43:21 UTC (7,578 KB)