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arxiv: 2606.22716 · v1 · pith:YWEFAOLHnew · submitted 2026-06-21 · 💻 cs.AI · cs.CL

Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards

Jungseob Lee , Seungyoon Lee , Seongtae Hong , Minhyuk Kim , Chanjun Park , Heuiseok Lim This is my paper

Pith reviewed 2026-06-26 10:09 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords ACOERGRPOreward collapsecorrect-only rewardsefficiency trainingmathematical reasoningtoken reductionlarge language models
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The pith

ACOER restricts length penalties to correct answers and adds dynamic normalization to prevent reward collapse while cutting tokens over 60 percent and raising accuracy.

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

The paper establishes that length penalties applied to incorrect answers in GRPO create divergent advantages through group normalization and drive reward collapse. Restricting penalties to correct answers removes this structural failure mode but still permits stochastic collapse from over-compression during sustained runs. ACOER solves both problems by isolating brevity bonuses to correct completions and introducing dynamic budget normalization together with control-loop penalty adjustments. On mathematical reasoning benchmarks the resulting models produce higher accuracy than the base model while generating more than 60 percent fewer tokens.

Core claim

ACOER eliminates the structural penalty loop by isolating brevity bonuses to correct completions and prevents stochastic compression via dynamic budget normalization and control-loop penalty adjustments, yielding stable efficiency-aware optimization that improves accuracy and reduces token generation by over 60 percent across math benchmarks.

What carries the argument

ACOER (Adaptive Correct-Only Efficiency Reward), which isolates length penalties exclusively to correct answers and applies dynamic budget normalization plus control-loop adjustments to block both collapse modes.

If this is right

  • Models trained with ACOER generate over 60 percent fewer tokens than the base model on mathematical reasoning benchmarks.
  • Overall accuracy on those benchmarks rises above the accuracy of the base model.
  • Training avoids both the structural collapse seen when incorrect answers receive length penalties and the stochastic collapse from unchecked over-compression.
  • Any reward configuration that penalizes length of incorrect answers remains structurally unstable under continued optimization.

Where Pith is reading between the lines

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

  • The same correct-only isolation pattern could be tested on non-mathematical tasks where response length also trades off against quality.
  • Control-loop adjustments might be generalized to other reward components that risk over-optimization in policy-gradient methods.
  • Longer training horizons would be needed to confirm that the dynamic normalization continues to bound compression without external intervention.

Load-bearing premise

That isolating length penalties to correct answers plus dynamic budget normalization and control-loop adjustments is sufficient to eliminate both the structural penalty loop and stochastic over-compression across sustained optimization runs.

What would settle it

A sustained GRPO training run using ACOER in which accuracy on a held-out math benchmark drops below the base model after the token reduction is achieved.

Figures

Figures reproduced from arXiv: 2606.22716 by Chanjun Park, Heuiseok Lim, Jungseob Lee, Minhyuk Kim, Seongtae Hong, Seungyoon Lee.

Figure 1
Figure 1. Figure 1: ACOER framework overview. Three mechanisms target the identified collapse pathways: (1) Correct-only signal (β=0) eliminates the β-loop (Pathway 1); (2) Adaptive budget normalization tracks the exponential moving average (EMA) of correct-answer lengths, preventing runaway feedback; (3) Control-loop α adaptation backs off efficiency pressure when accuracy drops, preventing Pathway 2 (correct-answer compress… view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy vs. efficiency for stable methods on [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Training dynamics for representative methods on MATH-500. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Training large language models to reason efficiently is a critical challenge. While integrating length-penalizing rewards into Group Relative Policy Optimization (GRPO) aims to reduce verbosity, it frequently triggers reward collapse, severely degrading reasoning capabilities. Through a systematic evaluation of various reward configurations, we identify the root mechanism: GRPO's group normalization creates divergent advantages when incorrect answers receive continuous length penalties. Consequently, methods penalizing the length of incorrect answers are structurally prone to collapse under sustained optimization. Furthermore, restricting penalties exclusively to correct answers avoids this primary failure, but leaves the model susceptible to a stochastic collapse driven by response over-compression. To robustly prevent both failure modes, we propose ACOER (Adaptive Correct-Only Efficiency Reward). ACOER eliminates the structural penalty loop by isolating brevity bonuses to correct completions and prevents stochastic compression via dynamic budget normalization and control-loop penalty adjustments. Evaluated across diverse mathematical reasoning benchmarks, ACOER improves overall accuracy compared to the base model while reducing token generation by over 60%, establishing a fundamentally stable approach for efficiency-aware optimization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that length-penalizing rewards in GRPO cause reward collapse when applied to incorrect answers because group normalization produces divergent advantages; restricting penalties to correct answers avoids the structural failure but risks stochastic over-compression. ACOER is proposed to isolate brevity bonuses to correct completions and add dynamic budget normalization plus control-loop penalty adjustments. The method is reported to raise accuracy over the base model while cutting token generation by more than 60% across mathematical reasoning benchmarks, yielding a stable efficiency-aware optimization procedure.

Significance. If the empirical results hold under scrutiny, the work supplies a concrete, easily implemented fix for a recurring instability in RL-based efficiency training of reasoning models. The explicit diagnosis of the group-normalization mechanism and the restriction to correct-only penalties constitute a useful contribution to reward design; the reported accuracy gains alongside large token savings would be practically relevant for deployment.

major comments (2)
  1. [Abstract] Abstract: the central claim of >60% token reduction together with accuracy improvement is presented without any quantitative benchmark scores, ablation tables, or per-task breakdowns; these data are load-bearing for the assertion that ACOER 'establishes a fundamentally stable approach.'
  2. [Abstract] The weakest assumption—that isolating length penalties to correct answers plus dynamic normalization suffices to eliminate both structural and stochastic collapse—is stated but not accompanied by sustained-run statistics or failure-mode frequency tables that would confirm the assumption holds across optimization trajectories.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the abstract. We address each major point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of >60% token reduction together with accuracy improvement is presented without any quantitative benchmark scores, ablation tables, or per-task breakdowns; these data are load-bearing for the assertion that ACOER 'establishes a fundamentally stable approach.'

    Authors: The abstract provides a concise summary of the claims, while the full manuscript includes the supporting quantitative benchmark scores, ablation studies, and per-task breakdowns in Section 4 and the appendix. To make the central claim more self-contained in the abstract, we will revise it to incorporate representative numerical results from the primary benchmarks. revision: yes

  2. Referee: [Abstract] The weakest assumption—that isolating length penalties to correct answers plus dynamic normalization suffices to eliminate both structural and stochastic collapse—is stated but not accompanied by sustained-run statistics or failure-mode frequency tables that would confirm the assumption holds across optimization trajectories.

    Authors: The reported experiments across multiple mathematical reasoning benchmarks demonstrate consistent accuracy improvements and token reductions with no observed collapse, providing empirical support for the stability of ACOER. We agree that explicit sustained-run statistics and failure-mode frequency tables would strengthen the presentation and will add such analysis of multiple independent runs in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents an empirical training method (ACOER) for stabilizing efficiency rewards in GRPO-based LLM reasoning optimization. The abstract and description frame the contribution as identification of failure modes in existing reward configurations followed by a proposed fix (correct-only penalties plus dynamic normalization), validated on mathematical benchmarks showing accuracy gains and >60% token reduction. No equations, closed-form derivations, or first-principles claims are provided that reduce the outcome to a fitted parameter or self-citation by construction. The work is self-contained as an experimental intervention with external benchmark evaluation; no load-bearing step equates the claimed result to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so free parameters, axioms, and invented entities cannot be enumerated from the manuscript; the method description implies at least one adaptive budget parameter and a control-loop adjustment whose values are not stated.

pith-pipeline@v0.9.1-grok · 5737 in / 1064 out tokens · 20559 ms · 2026-06-26T10:09:38.582210+00:00 · methodology

discussion (0)

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Reference graph

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