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REVIEW 2 major objections 1 minor 3 references

The answer from the strongest teacher is not necessarily the best supervision for a given student even when correct.

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2026-06-29 18:54 UTC pith:Q5YJVZOY

load-bearing objection SCAS shows student-matched answer selection can beat strongest-teacher distillation at scale, but the gradient proxy lacks direct validation against actual learning gains. the 2 major comments →

arxiv 2605.26872 v2 pith:Q5YJVZOY submitted 2026-05-26 cs.LG cs.AIcs.CL

The Strongest Teacher Is Not Always the Best Teacher: Student-Centric Answer Selection

Zhengyu Hu , Zheyuan Xiao , Linxin Song , Fengqing Jiang , Yuetai Li , Zhengyu Chen , Zhihan Xiong , Yue Liu
show 6 more authors
Junhao Lin Yao Su Lijie Hu Kaize Ding Teng Xiao Radha Poovendran
This is my paper
classification cs.LG cs.AIcs.CL
keywords knowledge distillationteacher selectionstudent-centricLLM supervisionsynthetic data generationanswer samplinggradient decompositionmodel training
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard practice in training language models picks the highest-performing teacher to generate supervision data, treating test performance as a proxy for teaching quality. This paper demonstrates that the assumption fails: multiple teachers can give correct answers, yet the strongest one's answer may not help a particular student the most. The authors introduce Student-Centric Answer Sampling to choose answers based on an estimated learning cost for the student. A forward-only proxy derived from token-wise gradient decomposition makes this selection efficient. Experiments with 30 teachers, 6 students, and 6 tasks show consistent gains, indicating that supervision should match the student rather than maximize teacher strength.

Core claim

The central claim is that teacher strength alone is not the right criterion for selecting supervision in distillation. Instead, answers should be sampled according to their student-centric learning cost, which can be approximated efficiently without backward passes. Applying this selection rule improves the student's final performance compared to always using the strongest teacher.

What carries the argument

Student-Centric Answer Sampling (SCAS) guided by a forward-only proxy for student-centric learning cost derived from token-wise gradient decomposition.

Load-bearing premise

The token-wise gradient decomposition yields an efficient forward-only proxy that accurately ranks answers by their actual benefit to the student's learning.

What would settle it

Running the full training with SCAS selection versus strongest-teacher selection on a new task where multiple correct answers exist and measuring if SCAS fails to improve or worsens student accuracy would falsify the claim.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Distillation performance improves when answers are chosen to minimize the student's learning cost rather than teacher capability.
  • The approach works across a wide range of teacher and student models and tasks.
  • Verified correct answers from weaker teachers can provide better training signal than those from stronger teachers.
  • Effective teacher supervision requires matching to the current student's state.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar selection could be applied when generating multiple answers from a single teacher model.
  • The proxy might help in designing adaptive curricula where difficulty is tuned per student.
  • Extending the method to chain-of-thought or tool-use data could yield further gains in reasoning tasks.
  • Large-scale training pipelines might benefit from per-student answer filtering at data generation time.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper claims that the strongest teacher is not always the best for a given student even when multiple teachers provide correct answers to the same question. It introduces Student-Centric Answer Sampling (SCAS), which selects among verified teacher answers using an efficient forward-only proxy for student-centric learning cost derived from token-wise gradient decomposition, and reports that this yields consistent student performance gains across 30 teacher models, 6 student base models, and 6 tasks.

Significance. If the proxy is shown to track actual learning benefit rather than incidental answer properties, the result would challenge the default practice of using the highest-performing teacher for distillation and could improve the efficiency of LLM training by prioritizing supervision matched to the student's current state.

major comments (2)
  1. [Abstract] Abstract: the central claim that SCAS improves supervision via student-centric selection rests on the token-wise gradient decomposition yielding a proxy that ranks answers by true benefit to the student, yet the abstract supplies no derivation details, no direct validation (e.g., correlation with loss reduction or gradient magnitude when different verified answers are substituted), and no evidence that observed gains arise from the intended mechanism rather than length, style, or diversity of the selected answers.
  2. [Experiments] Experiments: the reported 'consistent improvements' lack error bars, statistical tests, baseline comparisons beyond the strongest-teacher condition, and information on data exclusion rules, which are necessary to establish that the gains are reliable and attributable to the proxy rather than other factors.
minor comments (1)
  1. [Abstract] Abstract: a brief parenthetical on the specific tasks (e.g., math, coding, reasoning) or model scale ranges would help readers gauge the scope without needing to reach the experimental section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive comments on our work. We address each major comment below, providing clarifications from the manuscript and indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that SCAS improves supervision via student-centric selection rests on the token-wise gradient decomposition yielding a proxy that ranks answers by true benefit to the student, yet the abstract supplies no derivation details, no direct validation (e.g., correlation with loss reduction or gradient magnitude when different verified answers are substituted), and no evidence that observed gains arise from the intended mechanism rather than length, style, or diversity of the selected answers.

    Authors: The abstract serves as a concise overview, while the full derivation of the token-wise gradient decomposition is presented in Section 3. The manuscript includes empirical validation in Section 4, demonstrating that the proxy correlates with improved student performance across diverse settings, and analyses indicate that gains are not attributable to superficial factors like length or style. To better address this in the abstract, we will revise it to briefly note the proxy's basis and the mechanism validation. revision: partial

  2. Referee: [Experiments] Experiments: the reported 'consistent improvements' lack error bars, statistical tests, baseline comparisons beyond the strongest-teacher condition, and information on data exclusion rules, which are necessary to establish that the gains are reliable and attributable to the proxy rather than other factors.

    Authors: We agree that including error bars and statistical significance tests would strengthen the experimental section. We will add these to the revised manuscript. The strongest-teacher condition is the key baseline representing standard practice, with additional comparisons (e.g., random selection) provided in the appendix. Data exclusion rules for verified answers are detailed in the experimental setup (Section 4.1). These revisions will clarify the reliability of the results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in SCAS proxy derivation

full rationale

The paper presents the token-wise gradient decomposition as an independent first-principles motivation for deriving a forward-only proxy for student-centric learning cost. This derivation is not shown to reduce to a self-definition, a fitted parameter on the target data, or a self-citation chain. Experiments across 30 teachers, 6 students, and 6 tasks supply external performance evidence rather than tautological confirmation. No load-bearing uniqueness theorems or ansatzes imported from prior author work appear in the provided claims. The result is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no equations, implementation details, or parameter descriptions, preventing identification of free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 6588 in / 998 out tokens · 71172 ms · 2026-06-29T18:54:11.455754+00:00 · methodology

0 comments
read the original abstract

LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the highest-performing teacher to generate student training data, implicitly treating teacher test performance as a proxy for teaching quality. We show that this assumption can fail: even when multiple teachers provide correct answers to the same question, the answer from the strongest teacher is not necessarily the best supervision for a given student. To address this gap, we propose Student-Centric Answer Sampling (SCAS), a framework that selects from verified teacher-generated answers according to their estimated student-centric learning cost. Motivated by a token-wise gradient decomposition, we derive an efficient forward-only proxy for this cost and use it to guide answer selection during training. Experiments across 30 teacher models, 6 student base models, and 6 tasks show that SCAS consistently improves student performance, suggesting that effective distillation should prioritize supervision matched to the current student rather than teacher strength alone.

Figures

Figures reproduced from arXiv: 2605.26872 by Fengqing Jiang, Junhao Lin, Kaize Ding, Lijie Hu, Linxin Song, Radha Poovendran, Teng Xiao, Yao Su, Yue Liu, Yuetai Li, Zhengyu Chen, Zhengyu Hu, Zheyuan Xiao, Zhihan Xiong.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Framework overview of Student-Centric Answer Sampling (SCAS). 1 A verified answer pool is constructed from multiple teacher-generated answers for each question. 2 The current student estimates a forward-only student-centric learning cost for each candidate answer. 3 Candidates are ranked and grouped by the estimated cost, and low-cost answers are sampled to form a round-specific training set for updating t… view at source ↗
Figure 3
Figure 3. Figure 3: Data Efficiency Across Training Epochs. the only tie occurring on LiveBench for Qwen2.5- 3B. SCAS also remains strong on open-ended instruction-following benchmarks, obtaining the best IFEval score for all three model sizes and the best or tied-best LiveBench score across all set￾tings. These results support the central hypothesis of this work: among multiple correct teacher an￾swers, selecting answers tha… view at source ↗
Figure 4
Figure 4. Figure 4: Computing Efficiency for Selecting 1,000 Data Points. mental configuration follows Appendix A [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of the Number of Groups G in SCAS [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation of the Learning-cost Score. We vary λ over {0, 0.2, 0.4, 0.6, 0.8, 1.0} and eval￾uate on DeepScaleR and MATH with Qwen2.5-3B and Llama-3.2-3B. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Effect of the Trade-off Parameter λ. The number of groups G controls the granular￾ity of the stratified sampling step in Algorithm 1. Smaller G keeps the lowest-score group Π1(q) more diverse, but may introduce noise by including candidates whose learning costs are only moder￾ately low. Larger G makes Π1(q) more selective, but can also make sampling more sensitive to score noise. We study this effect on De… view at source ↗
Figure 8
Figure 8. Figure 8: Effect of the Representation Layer on Math. D Rank Correlation with Gradient Scores We evaluate whether SCAS preserves the ranking induced by exact gradient values. For each ques￾tion, we rank the candidate answers using each method and compute Spearman correlation with the ranking obtained from true gradient values; teacher-selection baselines follow the correspond￾ing teacher-model ranking. We average ov… view at source ↗
Figure 9
Figure 9. Figure 9: Answer-Union Scaling with Strong Teachers. Top-k merges k teachers’ verified answers, giving k training answers per retained question; SCAS uses one selected answer per question. The shaded band shows one standard deviation. teacher, but the gains are not enough to match SCAS. For Qwen2.5-0.5B, the answer-union base￾line increases from 20.07 at top-1 to 24.21 at top-9, still 3.41 points below SCAS. For Qwe… view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

3 extracted references · 2 canonical work pages · 1 internal anchor

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