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ARXIV:2605.11182 · LLM TRAINING/FINE-TUNING · SUBMITTED 13 MAY · 20:19 UTC · FRESHNESS FRESH
ARXIV:2605.11182LLM TRAINING/FINE-TUNINGSUBMITTED 13 MAY · 20:19 UTCFRESHNESS FRESHSiqi Zhu · Xuyan Ye · Hongyu Lu · Weiye Shi · Ge Liu · arXiv
An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies.
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Pain An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies.
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An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies. However, existing results on their effectiveness remain mixed: while OP(S)D has shown…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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…
LLM Training/Fine-tuning moved forward this cycle; last verified May 2026. Public score 3.0/10.
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An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies.
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10.48550/arXiv.2605.11182An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies.
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.
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An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies. However, existing results on their effectiveness remain mixed: while OP(S)D has shown promise in system pr...
METHOD
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 effective...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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.
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LLM Training/Fine-tuning moved forward this cycle; last verified May 2026. Public score 3.0/10.
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An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training/Fine-tuning moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An empirical study analyzing the pitfalls and mechanisms of on-policy distillation for large language models, identifying failure modes and proposing mitigation strategies.
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