Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation explores An offline distillation framework for large language models that significantly speeds up post-training for reasoning and code generation tasks without requiring a live teacher server.. Commercial viability score: 7/10 in LLM Post-Training.
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Canonical ID lightning-opd-efficient-post-training-for-large-reasoning-models-with-offline-on-policy-distillation | Route /paper/lightning-opd-efficient-post-training-for-large-reasoning-models-with-offline-on-policy-distillation
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/buildability/lightning-opd-efficient-post-training-for-large-reasoning-models-with-offline-on-policy-distillation
Subject: Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation
Verdict
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Receipt path
/buildability/lightning-opd-efficient-post-training-for-large-reasoning-models-with-offline-on-policy-distillation
Paper ref
lightning-opd-efficient-post-training-for-large-reasoning-models-with-offline-on-policy-distillation
arXiv id
2604.13010
Generated at
2026-04-15T16:58:36.846Z
Evidence freshness
stale
Last verification
2026-04-15T16:58:36.846Z
Sources
4
References
0
Coverage
67%
Lineage hash
073dee7a3e2e837b4738726e628bc838ed55d054d4784d056cff4bd36c86e9b7
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Pending verification refs / 4 sources / Verification pending
references
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. decomposition, βπ½on(π) = Eπ₯βΌπref[π€(π₯; π) Β· βπ½off(π)] where π€(π₯; π) = ππ(π₯)/πref(π₯), so the offline gradient is
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This equation captures one of the core mathematical components of the system. computed as π΄π‘(π) = log ππ(ππ‘| π π‘) βlog ππ(ππ‘| π π‘), where the teacher term is read directly from πOPD and
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This equation captures one of the core mathematical components of the system. π΄π‘= log ππ(ππ‘| π π‘) βlog ππ(ππ‘| π π‘) is computed by reading log ππfrom πOPD while computing log ππonline.
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