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Life sciences · Preprint
arXiv · August 12, 2026
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REOPD is a proposed adaptive coefficient method for on-policy distillation that adjusts token-level reward extrapolation based on teacher reliability. The authors report improvements over G-OPD on multi-teacher and single-teacher mathematics tasks, with matching performance on code tasks, but the work is unreviewed and lacks quantitative effect sizes, confidence intervals, or statistical significance testing.
Preprint. Mathematics and code generation benchmarks in on-policy distillation framework. Intervention: REOPD: reliability-adaptive reward extrapolation with token-wise adaptive coefficient λ_{b,t}=1+γ_b q_t. Compared with: G-OPD (baseline on-policy distillation with global coefficient).
REOPD outperforms G-OPD on single-teacher mathematics and on both domains in the multi-teacher setting REOPD matches G-OPD on single-teacher code generation Proposes token-wise adaptive coefficient λ_{b,t}=1+γ_b q_t that combines batch-level and token-level adaptation
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This is a methodological paper presenting a novel algorithmic framework (REOPD) with empirical validation on benchmark tasks, but lacks peer review, clinical or real-world deployment data, and independent replication.
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On-policy distillation (OPD) trains a student on its own trajectories under dense token-level supervision from a teacher. Reward-extrapolation methods such as ExOPD amplify the teacher-reference log-likelihood ratio to move beyond direct imitation, but apply a single global coefficient $λ$ to every token. This can drive the student to fit extreme peaks in the implicit reward, causing reward hacking and unstable training, and the optimal $λ$ varies across domains, requiring costly sweeps. We propose REOPD, a reliability-adaptive reward extrapolation framework for OPD. REOPD combines a token-level compatibility weight with a batch-level adaptive budget, yielding a token-wise coefficient $λ_{b,t}=1+γ_b q_t$ that preserves teacher alignment while selectively extrapolating along reliable teacher-reference directions. It requires no verifier, reward model, value model, or extra rollout beyond standard OPD. REOPD outperforms G-OPD on single-teacher mathematics and on both domains in the multi-teacher setting, while matching G-OPD on single-teacher code, demonstrating effective fine-grained reliability adaptation across domains and teacher configurations.
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