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Life sciences · Preprint
arXiv · August 11, 2026
Early or partial results. Treat as a signal, not a conclusion.
ReOrder-OPD is a prompt-ordering heuristic for on-policy distillation that sorts training prompts by an estimated teacher continuation reliability proxy (maximum ROUGE-5 F1 score). Oracle experiments show monotonic association between the proxy and true reliability across decile bins, and empirical gains are reported across six model and domain settings, but the work lacks peer review, statistical significance testing, matched comparator baselines, and sample size reporting.
Algorithmic method proposal with oracle validation and empirical comparison. Language models (Qwen3, Gemma4) on mathematics and code generation tasks; no human subjects enrolled.. Intervention: ReOrder-OPD: prompt ordering by maximum ROUGE-5 F1 proxy score before on-policy distillation training.. Compared with: Random prompt ordering, ascending-R ordering, and unordered vanilla OPD (FiRe-OPD and ExOPD variants)..
Oracle experiments show that high-R prompts yield larger OPD gains and that descending-R training outperforms random and ascending orders on a fixed prompt pool. Across ten equal-frequency bins of the ROUGE-5 F1 proxy score, mean R rises monotonically, supporting coarse reliability separation. ReOrder-OPD improves every matched aggregate comparison across Qwen3 and Gemma4 mathematics settings and Qwen3 code settings (six FiRe-OPD and ExOPD settings total).
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A single-centre, uncontrolled algorithmic study with proxy-based validation on a fixed prompt pool; gains are reported but lack matched baselines and statistical significance testing, making this an early-stage method proposal requiring peer review and independent replication.
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On-policy distillation (OPD) applies token-level teacher supervision to student-generated trajectories, but this supervision is not always reliable. Existing methods use local confidence or teacher-student agreement to weight, filter, or truncate the sampled trajectory. These signals do not directly determine whether the teacher can continue a student prefix to a correct answer, and trajectory-level interventions can conflate one rollout's unreliability with low expected training value of its prompt. We define prompt-level teacher continuation reliability $R$ as the teacher's probability of reaching a correct answer from a student prefix, averaged over prefixes and trajectories induced by the current student. Oracle experiments show that high-$R$ prompts yield larger OPD gains and that descending-$R$ training outperforms random and ascending orders on a fixed prompt pool. Because estimating $R$ requires many teacher continuations, we use the maximum ROUGE-5 F1 between one independent student rollout and verifier-correct same-prompt teacher trajectories. Across ten equal-frequency bins of this actual score, mean $R$ rises monotonically, showing that the proxy separates coarse reliability levels. ReOrder-OPD sorts prompts by the proxy, then draws independent on-policy training trajectories for vanilla OPD. It improves every matched aggregate comparison across Qwen3 and Gemma4 mathematics settings and Qwen3 code settings. Gains in all six FiRe-OPD and ExOPD settings show that prompt ordering complements within-trajectory supervision.
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