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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes a four-coefficient parameterization for per-token gating in on-policy knowledge distillation that unifies existing methods (EOPD and ToDi) and adds multi-channel and bias degrees of freedom. A single-seed empirical sweep on sentiment classification tasks shows improved accuracy in 33 of 36 comparable configurations, but targeted three-seed replications of headline results are directionally consistent but underpowered and not individually significant; the work is presented as an exploratory methodological framework rather than a validated finding.
Exploratory empirical comparison with targeted three-seed replication. Qwen3 models (teacher 32B, student 4B) on TweetEval emotion, hate, and offensive classification tasks.. Intervention: Four-coefficient per-token gating parameterization with multi-channel composition and explicit bias term, compared against single-channel (entropy-only or gap-only) 1D gating restrictions.. Compared with: Matched-magnitude single-channel 1D restrictions; effective-KL-matched static baselines..
Full parameterization configurations reached higher accuracy than matched-magnitude single-channel 1D restrictions in 33 of 36 comparable cells on TweetEval emotion and hate tasks. Dynamic gating placed ahead of effective-KL-matched static baselines in 19 of 26 cells in mean-match isolation experiment. Targeted three-seed paired replications of nine headline comparisons were directionally consistent but individually smaller than single-seed estimates and not significant at n=3.
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Early-phase methodological comparison of gating parameterizations in knowledge distillation with exploratory aggregate evidence from a single-seed sweep, targeted replications underpowered (n=3), and results presented as a coordinate system rather than definitive findings.
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Per-token gating of forward/reverse KL losses has become a standard technique for on-policy knowledge distillation (OPD), but existing methods such as EOPD (Jin et al., 2026) and ToDi (Jung et al., 2025) each fix a single gating signal and a single gating direction, and the two have never been compared directly. We introduce a four-coefficient parameterization lambda_t = sigma(a * h_t + b * u(x) + c + d * gap_t) in which direction-aligned proxies of EOPD and ToDi appear as one-dimensional (1D) restrictions, and which adds multi-channel composition and an explicit bias as further degrees of freedom. On TweetEval (Barbieri et al., 2020) emotion and hate, with a Qwen3-32B teacher and a Qwen3-4B student, configurations in the full family reach higher accuracy than the matched-magnitude single-channel (entropy-only / gap-only) 1D restrictions in 33 of 36 comparable cells, and a 26-cell mean-match isolation experiment places dynamic gating ahead of effective-KL-matched static baselines in 19 of 26 cells. Because cells share training data, models, and parameter substructure, we report both counts as exploratory aggregate directional evidence rather than as independent hypothesis tests. Targeted three-seed paired replications of the nine headline comparisons singled out by that sweep -- including a third task, offensive -- are directionally consistent, but individually smaller than the single-seed estimates and not significant at n=3. We therefore present the parameterization primarily as a shared coordinate system for comparing per-token gating designs in short-output classification OPD.
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