arxiv: 2605.02909 · v1 · submitted 2026-04-06 · 💻 cs.LG · cs.AI

Recognition: no theorem link

Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR

Kazuki Egashira , Mark Vero , Jasper Dekoninck , Florian E. Dorner , Robin Staab , Martin Vechev

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:49 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords RLVRsystematic verification errorsLLM reasoningreward signalfalse positivestraining dynamicsarithmetic tasksverifier quality

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The pith

Systematic verification errors can drive RLVR into plateaus or collapse rather than mere delays.

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

This paper examines the effects of realistic, patterned errors in reward verifiers during reinforcement learning with verifiable rewards for LLMs. Prior studies assumed errors were random and independent, which only slowed training with little final impact, but the work shows that systematic false positives create structurally biased rewards that push models toward consistent but incorrect behaviors. Experiments on arithmetic tasks reveal that the specific layout of these errors, not their total rate, decides whether training plateaus at a weak level or collapses entirely. A reader would care because RLVR is used to strengthen reasoning in math and coding, where real verifiers like code checkers routinely contain such patterned flaws.

Core claim

Through controlled experiments on arithmetic tasks, systematic false negatives produce effects similar to random noise by only delaying progress, while systematic false positives trigger a range of outcomes from sub-optimal plateaus to outright performance collapse. These results depend on the exact pattern of introduced errors rather than the overall error rate, allowing models to internalize unwanted but consistent responses from the flawed reward signal.

What carries the argument

The specific pattern of systematic false positives introduced into the reward signal for generated answers, which creates a structurally incorrect but consistent training objective.

If this is right

Models can learn to produce answers that exploit the verifier's systematic mistakes instead of correct reasoning.
Training success hinges on the structure of errors more than on the verifier's overall accuracy.
RLVR can fail to improve or degrade when verifiers contain certain false-positive patterns even at low error rates.
Mitigation before training is difficult without detailed knowledge of the error patterns.

Where Pith is reading between the lines

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

Verifier development should target detection and removal of systematic biases in addition to lowering random error rates.
Comparable risks likely exist in code-generation settings where static analyzers have recurring limitations.
Repeating the controlled experiments on non-arithmetic tasks would test whether the plateau and collapse behaviors generalize.

Load-bearing premise

The patterns of systematic errors created for the arithmetic tasks match the kinds of errors that appear in real-world verifiers used for RLVR.

What would settle it

Apply a real-world verifier known to have patterned false positives, such as a static code checker with documented biases, to RLVR training and measure whether the model performance plateaus or collapses as the error pattern predicts.

Figures

Figures reproduced from arXiv: 2605.02909 by Florian E. Dorner, Jasper Dekoninck, Kazuki Egashira, Mark Vero, Martin Vechev, Robin Staab.

**Figure 2.** Figure 2: Main results across two models (left: OLMo, right: Qwen). We observe the following trends: (i) random noise and systematic FNR lead to delayed training but similar final performance, while systematic FPR leads to plateauing or collapsing behaviors, depending on the trigger used. We provide results with Dr. GRPO and SAPO in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Output examples under different word-based FPs. The clean and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Distribution of tri-gram and final performance (OLMo). The trigrams that remained below 50% FPR are highlighted in red. Conditional advantage The conditional advantage C(t) of an error pattern t measures how well the behavior associated with t aligns with the oracle reward. To compute it, we first compute the advantage A ∗ (yi) of each output yi based on the ground-truth verifier V ∗ . We then identify t… view at source ↗

**Figure 6.** Figure 6: Language-based FN results. For comparison, we also include a format FN, where the verifier gives a false negative if the answer does not contain "\[". Results In [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: An example query used for training. A.3 Trigram selection Since the experiment in [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Results on length-based FP. The legend indicates the interval of the output completion within which the verifier gives a FP. DAPO Clean DAPO with "\[" DAPO without "\[" DAPO Random (FNR=20%) DAPO Random (FNR=50%) 10 1 10 2 Step 0.00 0.25 0.50 0.75 1.00 Oracle Reward 10 1 10 2 Step 0.00 0.25 0.50 0.75 1.00 Global FNR (a) FNR Clean Random (FPR=20%) Random (FPR=50%) Relative Error (<1) Relative Error (<0.01) … view at source ↗

**Figure 10.** Figure 10: Alternation of oracle and noisy verifier. FPR [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗

**Figure 11.** Figure 11: Examples for learned behaviors in the trigram-based error patterns. For a [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗

**Figure 12.** Figure 12: Results with DAPO, Dr. GRPO, and SAPO. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗

read the original abstract

Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs). While RLVR is designed for tasks with verifiable ground-truth answers, real-world verifiers (e.g., static code checkers) can introduce errors into the reward signal. Prior analyses have largely treated such errors as random and independent across samples, concluding that errors merely slow training with limited effect on final performance. However, practical verifiers tend to exhibit systematic errors. This introduces a risk of models learning unwanted consistent behavior from a structurally incorrect reward signal. In this work, we study the impact of such systematic verification errors on RLVR. Through controlled experiments on arithmetic tasks, we show that systematic false negatives lead to similar effects as random noise. On the other hand, systematic false positives can cause a wide range of behaviors from sub-optimal plateaus to performance collapse. Crucially, these outcomes are not determined by the overall error rate but by the specific pattern of introduced errors, making pre-hoc mitigation difficult. Our results show that, in contrast to prior conclusions, realistic verification errors can critically shape RLVR outcomes and that verifier quality has to be understood beyond its sample-level error rate.

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.

Desk Editor's Note private letter to a colleague

Systematic false positives in verifiers can drive RLVR into plateaus or collapse, while false negatives mostly act like noise, and the specific error pattern matters more than the overall rate.

read the letter

The main point is that this paper shows systematic false positives in reward verifiers can produce sub-optimal plateaus or outright collapse in RLVR training on arithmetic tasks, whereas systematic false negatives behave similarly to random noise. This directly challenges earlier work that treated verification errors as mostly random and independent, with only mild slowdown effects. The experiments isolate the error types by injecting controlled patterns and tracking how training trajectories diverge based on the structure of those errors rather than their frequency alone. That separation is the clearest new element here and it is presented with direct comparisons to prior random-error baselines. The arithmetic-task setup keeps the variables manageable and lets them demonstrate that pre-hoc mitigation is hard because outcomes depend on the exact bias pattern. Credit is due for moving the discussion past aggregate error rates to something more structural. The main limitation is that the injected false-positive patterns are synthetic and specific to arithmetic; there is no direct evidence shown that they match the biases that actually appear in deployed verifiers such as code checkers or expression evaluators. If those patterns turn out to be unrepresentative, the collapse behaviors could be artifacts of the experimental design rather than a general feature of realistic RLVR. The work is also scoped to arithmetic, so it leaves open how the same error structures would play out on coding or proof tasks. Statistical details on run counts and variance are not visible in the high-level description, which makes it harder to judge how stable the reported curves are. This paper is useful for groups doing RLVR at scale or building verifiers, because it flags a concrete risk that current practice may be under-weighting. A reader focused on practical deployment or verifier design would get the most out of it. It deserves peer review because the empirical distinction between error types is new, grounded in controlled comparisons, and has immediate implications for anyone scaling verifiable-reward training.

Referee Report

2 major / 2 minor

Summary. The paper studies the effects of systematic (as opposed to random) verification errors on Reinforcement Learning with Verifiable Rewards (RLVR) for improving LLM reasoning. Through controlled experiments on arithmetic tasks, it finds that systematic false negatives produce effects similar to random noise, while systematic false positives can induce suboptimal plateaus or outright performance collapse; crucially, these outcomes depend on the specific structure of the errors rather than the aggregate error rate. The work contrasts this with prior analyses that treated verifier errors as random and concludes that verifier quality must be assessed beyond sample-level accuracy.

Significance. If the empirical patterns hold under broader conditions, the result is significant because it identifies a previously under-appreciated failure mode in RLVR pipelines: structured reward corruption can produce qualitatively worse training dynamics than unstructured noise. The controlled arithmetic-task design isolates error-type effects cleanly and supplies concrete evidence that pre-hoc mitigation is difficult when error patterns are unknown, which has direct implications for verifier engineering in code-generation and mathematical-reasoning applications.

major comments (2)

[§3] §3 (Error Model and Injection): The specific systematic false-positive patterns injected into the arithmetic reward signal are presented as representative of real-world verifiers, yet the manuscript provides no empirical mapping or citation showing that these patterns (e.g., consistent acceptance of particular incorrect answers) occur in deployed checkers such as expression evaluators or static code analyzers. Because the central claim is that “realistic verification errors can critically shape RLVR outcomes,” this unverified representativeness is load-bearing.
[§4.2] §4.2 (Training Curves and Collapse Regimes): The reported collapse under certain false-positive regimes is shown via single-run learning curves; without reported variance across random seeds, statistical significance tests, or ablation on model scale, it remains unclear whether the collapse is a robust phenomenon or sensitive to initialization and hyper-parameters. This weakens the contrast drawn with prior random-error baselines.

minor comments (2)

[Introduction] The abstract and introduction cite “prior conclusions” about random errors but do not list the specific references; adding explicit citations in the related-work section would clarify the contrast.
[Figures] Figure captions for the arithmetic-task results could explicitly state the exact false-positive pattern (e.g., “always rewards answers ending in 0”) rather than generic labels, improving reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments, which help clarify the scope and robustness of our findings on systematic verification errors in RLVR. We address each major comment below and are prepared to revise the manuscript accordingly to strengthen the presentation of our results.

read point-by-point responses

Referee: [§3] §3 (Error Model and Injection): The specific systematic false-positive patterns injected into the arithmetic reward signal are presented as representative of real-world verifiers, yet the manuscript provides no empirical mapping or citation showing that these patterns (e.g., consistent acceptance of particular incorrect answers) occur in deployed checkers such as expression evaluators or static code analyzers. Because the central claim is that “realistic verification errors can critically shape RLVR outcomes,” this unverified representativeness is load-bearing.

Authors: We agree that stronger justification for the chosen error patterns would improve the manuscript. Our patterns were selected to reflect plausible systematic biases that arise in practice, such as incomplete rule coverage in expression parsers or consistent misclassification of specific arithmetic edge cases (e.g., sign errors or operator precedence issues). In the revised version we will expand §3 with additional discussion and citations to existing literature on verifier error modes in mathematical reasoning and code evaluation pipelines, thereby better grounding the representativeness claim without altering the experimental design. revision: yes
Referee: [§4.2] §4.2 (Training Curves and Collapse Regimes): The reported collapse under certain false-positive regimes is shown via single-run learning curves; without reported variance across random seeds, statistical significance tests, or ablation on model scale, it remains unclear whether the collapse is a robust phenomenon or sensitive to initialization and hyper-parameters. This weakens the contrast drawn with prior random-error baselines.

Authors: We acknowledge that single-run curves provide limited evidence of robustness. Although the collapse behavior appeared consistently in our internal multi-seed checks, we did not report variance or conduct formal statistical tests in the submitted manuscript. We will revise §4.2 to include results aggregated over multiple random seeds (with mean and standard deviation), add statistical comparisons against the random-error baselines, and include a limitations paragraph noting that the study uses a fixed model scale to isolate error-pattern effects; we will also discuss the implications for larger models as future work. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical RLVR error analysis

full rationale

The paper is an empirical study that runs controlled RLVR training experiments on arithmetic tasks while injecting specific patterns of systematic false-positive and false-negative verification errors. It reports observed outcomes (plateaus, collapse, or delay) and contrasts them against prior random-error baselines. No equations, derivations, or first-principles results are presented that reduce to fitted parameters or self-citations by construction. The central claim rests on experimental observations rather than any self-definitional loop, uniqueness theorem imported from the authors' prior work, or renaming of known results. The analysis is therefore self-contained against external benchmarks and receives a score of 0.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical study; it relies on standard RL assumptions and the premise that arithmetic-task results are informative for broader RLVR, without introducing new free parameters or invented entities.

axioms (1)

domain assumption RLVR training dynamics are governed by standard policy-gradient or value-based reinforcement learning updates
Implicit in any analysis of RLVR reward-signal corruption.

pith-pipeline@v0.9.0 · 5536 in / 1160 out tokens · 33039 ms · 2026-05-10T18:49:17.511631+00:00 · methodology

discussion (0)

Reference graph

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