Hindsight Compacts but Does Not Repair: Rethinking On-Policy Self-Distillation in Reasoning Models
On-Policy Self-Distillation (OPSD) has emerged as a promising post-training method: requiring only privileged context, it enables token-level credit assignment from a self-teacher with hindsight, yielding higher accuracy with shorter responses. However, in thinking-enabled mathematical reasoning, where solving a problem takes long reasoning traces that explore, reflect, and backtrack, its length reductions persist but its accuracy gains largely do not. Does OPSD's hindsight signal, then, still repair failed trajectories, or does it only compact viable ones? We hypothesize compaction: in long traces, hindsight reveals which steps were unnecessary more readily than which steps would have re...
| Authors | Jaehoon Kim, Dongha Lee |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 26 September 2026 |
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