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REVIEW 3 major objections 4 minor 21 references

Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read RL fine-tuning boosts clean-benchmark reasoning but fails under noisy or multi-option inputs, and this paper measures that gap.

desk verdict The paper's own Table 7 contradicts its headline claim of decline across all three scenarios, and the summary-inference comparison is confounded; the underlying datasets and noise-subsetting idea are worth a second look. read the letter →

arxiv 2508.04848 v1 pith:KETDJT24 submitted 2025-08-06 cs.AI

Chang Tian , Matthew B. Blaschko , Mingzhe Xing , Xiuxing Li , Yinliang Yue , Marie-Francine Moens This is my paper
classification cs.AI
keywords reinforcementlearningGRPOreasoningevaluationnoiserobustnesssummaryinferencecontextualfilteringLLMdistractor
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper is trying to establish that reinforcement-learned reasoning gains do not survive contact with realistic imperfections: once a fine-tuned model is asked to consider several possibilities before concluding, to suppress fine-grained distractors, or to ignore irrelevant context, its accuracy drops below its own clean-input performance. The authors define three such non-ideal scenarios, fine-tune three LLMs and one LVLM with GRPO, and evaluate on eight datasets. The consistent degradation across models and datasets is presented as evidence that RL fine-tuning mostly cements basic reasoning on clean prompts and leaves advanced reasoning—robust reasoning under imperfect input—underdeveloped. The paper also tests two remediation strategies, format rewards and example guidance, which recover part of the loss but do not close the gap.

What carries the argument

The argument is carried by two mechanisms. First, GRPO, a group-relative policy gradient objective: for each question it samples a group of outputs and uses the per-group normalized advantage $A_i = (r_i - \mathrm{mean}(\{r_1,\dots,r_G\})) / \mathrm{std}(\{r_1,\dots,r_G\})$ with a KL-divergence penalty to the reference policy; the reward $r_i$ is the sum of an output-format reward and an output-correctness reward. Second, a staged training/evaluation design that keeps the model identical while changing only the input or the prompt: Stage A trains the standard reasoning format, Stage C adds the summarize-every-option format, Stages E/G differ by the presence of a guiding example, and evaluati

What would settle it

Run the same Stage D evaluation but replace the 'analyze each option then summarize' instruction with a control instruction that is matched in length and in the number of reasoning steps, e.g., 'analyze option A in as much detail, then write a short conclusion about option A only'. If the Stage AB to Stage AD accuracy drop reappears with this control, the deficit is a general sensitivity to long multi-part prompts and not a summary-inference capacity; if it disappears, the summary-inference reading is supported. A second check: prepend to a FineTest question an explicit sentence, 'Ignore all i

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Extended reading notes

Core claim

The central claim is that RL-fine-tuned language models show a significant, reproducible accuracy drop when moved from ideal test conditions to three non-ideal ones: summary inference (Stage B to Stage D, where evaluation adds the instruction to analyze each option and then summarize), fine-grained noise suppression (clean TestA to FineTest), and contextual filtering (clean TestB to FilterTest). The paper reports, for instance, Llama3.1 falling from 69.8 to 64.9 percent on CommonsenseQA and Qwen2.5-VL falling from 60.2 to 48.8 percent on SciVQA under the Stage D instruction, and similar degradations for Mistral and Qwen3. In the noise scenarios, RL fine-tuning does improve ideal-condition ac

Load-bearing premise

The paper assumes that the performance drop caused by adding the Stage D instruction—write an analysis for each option, then summarize—is a measure of missing reasoning ability, even though the instruction is not compared against a control instruction of similar length and complexity that does not require summary inference.

Editorial extensions

If this is right

  • Current RL fine-tuning evaluation practice, which reports accuracy on clean prompts, overstates real-world reasoning robustness: gains measured on GSM8K/MATH-style clean inputs do not transfer to prompts with distractors, irrelevant context, or multi-option summary instructions.
  • If the deficit is a genuine advanced-reasoning failure, then post-training recipes should include non-ideal scenarios in the reward or curriculum; the paper's format reward and example guidance are initial, partial evidence that training-time interventions can recover some robustness.
  • The consistent Stage AB to Stage AD drop across all four models implies that merely instructing a model to reason about all options is harmful, not helpful, to its accuracy—a counterintuitive result that directly contradicts the intuition that more consideration should improve reasoning.
  • The pattern of remediation working only in some model-scenario combinations (e.g., example guidance helping Qwen3 for fine-grained noise but hurting Llama3.1) suggests that the mechanism of robustness is model-specific and not yet a general recipe.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The Stage AB/AD drop is consistent with an alternative explanation the paper does not rule out: the longer, multi-part Stage D instruction may simply be a prompt-format the model was never trained on, and the drop could reflect instruction-following brittleness rather than a reasoning deficit. A length-matched and step-matched control instruction that asks for analysis of a single option would sep
  • The fine-grained noise and contextual filtering results point toward a common failure mode: RL fine-tuning with correctness rewards on clean prompts can reinforce shallow cue-matching, so the model never learns an explicit filter; an explicit 'ignore the noise' instruction in the prompt is a directly testable extension that the current evaluation does not include.
  • If the datasets contain near-duplicates of commonly seen benchmark questions, part of the ideal-condition gain could be memorization-like pattern reuse rather than generalizable reasoning, which would further explain why noisy variants break the performance; contamination-controlled evaluation is a natural follow-up.
  • The paper's framing in terms of human brain-science findings suggests a testable analogy: humans maintain accuracy under imperfect input through attentional filtering; a model trained with an explicit reward for identifying and setting aside distractors (rather than just for the final answer) should generalize to unseen noise types if the deficit is truly in filtering.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper investigates whether RL fine-tuning with GRPO improves LLM/LVLM reasoning in realistic non-ideal conditions. It defines three scenarios: summary inference (per-option analysis followed by a conclusion), fine-grained noise suppression, and contextual filtering. The authors train three LLMs and one LVLM under different combinations of format rewards and example guidance (Stages A–H), evaluate on public datasets plus two newly constructed noisy sets, and claim that although RL improves performance on clean benchmarks, it degrades significantly in all three non-ideal scenarios. They also propose remediation strategies (StageCD, StageEH, StageGH).

Significance. The question is important: most RL reasoning evaluations use clean prompts, and evidence on transfer to realistic, noisy, multiple-possibility inputs is scarce. The paper offers a useful taxonomy and a multi-model/multi-dataset sweep, and it is transparent about run-to-run variability. However, the headline claim is not supported by the paper's own tables for contextual filtering, and the summary-inference deficit is confounded by instruction format. If these issues were corrected, the finding that RL fine-tuning does not uniformly improve noisy-input reasoning would still be of interest, but the current version overstates its conclusions.

major comments (3)
  1. [Abstract; Contribution 2; Table 7] The claim that performance 'declines significantly across all three non-ideal scenarios' is internally inconsistent with Table 7. For contextual filtering, Qwen3-StageEF(b) improves over Qwen3-StageEF(a) on Math12k (56.53→57.27) and MathReasoning (68.26→68.93), Llama3.1 improves on Math12k (40.80→42.89), and Qwen2.5-VL is essentially flat on MathVision (20.00→19.86). The paper's own 'Contextual Filtering Evaluation' section says 'most models maintain their performance... with the sole exception of Mistral.' Thus one of the three scenarios shows the opposite of the claimed pattern for most models. The abstract and contributions must be revised to scenario-specific claims.
  2. [Method, Summary Inference (Stage D); Tables 3–4] The StageAB-to-StageAD comparison is a load-bearing test of 'summary inference' ability, but it changes instruction length and required output format simultaneously: StageD adds the long per-option analysis instruction, while StageB does not. A drop such as Qwen2.5-VL MathVision (15.73→10.07) or Llama3.1 Ceval-exam (49.42→39.00) could reflect format sensitivity rather than limited reasoning. In addition, StageC trains the model on exactly the format that StageD requests, so the StageCD recovery is partly format-matching by construction. A length-matched control instruction that does not require summarization, and an evaluation of StageCD with a paraphrased instruction, are needed to support RQ1. Note also that the text claims a 'consistent drop ... across all models,' but Qwen3-StageAD improves on CommonsenseQA (74.80→75.40).
  3. [Appendix references throughout] Several load-bearing methodological details are promised but absent: the 'math explanation' for summary inference, the construction and crowdsourcing protocol for FineTest/FilterTest, the guiding example text, and model-specific training details are all referred to appendices that do not appear in the manuscript. Without the noisy-set construction details and exact prompts, the experiments cannot be reproduced or the remediation claims checked. If appendices exist, they must be included; otherwise the details need to be in the main text/supplement.
minor comments (4)
  1. [Abstract / Code release] The code and data link is a placeholder ('https://github.com/xxxx'); provide the final URL.
  2. [Figure 2 / Stage F labels] The text introduces Stage F(a) and F(b) but Figure 2 does not clearly depict the F(a)/F(b) distinction; please annotate the figure accordingly.
  3. [Tables 3–4 captions] The phrase 'Model and StageAB omit summary inference style' is confusing; specify that both are evaluated under StageB.
  4. [Terminology] The phrase 'advanced reasoning capabilities' is used as an unexplained construct; provide an operational definition tied to the evaluation tasks to avoid circular reasoning.

Circularity Check

1 steps flagged · score 4.0 of 10

Remediation stage C trains exactly the output format used in the Stage D evaluation, making the recovery partly a format-matching artifact rather than evidence of restored reasoning.

  1. fitted input called prediction [Method, Summary Inference (Stage C and Stage D descriptions); Figure 2 (Rtrain = Rf + Rc)]
    "In stage C, we train the model to assume each possible answer as correct in turn, perform a brief analysis for each, and then perform summarizing reasoning based on all intermediate analyses. ... Similarly, in the evaluation process, the instructions for Stage D explicitly require the model to perform a brief analysis for each option followed by the summarized inference."

    Stage C training uses a format reward (Rf, see Figure 2: 'Rtrain = Rf + Rc') that enforces exactly the output structure that Stage D evaluation explicitly requests: per-option analyses followed by a summary. Therefore, the StageCD model is optimized to produce precisely the format the StageD test demands. Any improvement from StageAD to StageCD is thus in part a direct consequence of training with the evaluation format as a reward, not a demonstration of acquired 'advanced reasoning' or 'summary inference' ability. The paper interprets the StageCD recovery as confirming the existence of reasoning deficits, but the evaluation format is itself the training target, making the recovery partially a fitted-input artifact. No control is provided that isolates format compliance from deeper reasoni

full rationale

The primary circularity is in the summary-inference remediation: Stage C training explicitly trains the model to output the same per-option-analysis-plus-summary structure that Stage D evaluation requires, and does so via a format reward. Thus, StageCD performance is partly determined by the training objective matching the test instruction, and the paper's conclusion that the remediation 'restores performance' and 'enhances summarization and advanced reasoning' is weakened because the format is trained directly. This is a genuine reduction of a claimed discovery to a training design choice. Other potential concerns are not circularities in the technical sense: the StageAB-to-StageAD drop confounds instruction length and output format, but that is a construct-validity threat, not an equivalence to inputs; the abstract's claim that performance 'declines significantly across all three non-ideal scenarios' is contradicted by the paper's own Table 7 for contextual filtering (e.g., Qwen3 and Llama3.1 improve), which is an internal inconsistency, not a circular derivation. The paper is otherwise self-contained against external datasets and does not rely on load-bearing self-citations. Hence the circularity score is moderate, reflecting the partial format-matching in the remediation but recognizing that the central deficit claim still has independent empirical content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The paper rests on a small set of hand-chosen training and reward-design parameters, an untested assumption that the instruction change isolates a latent reasoning trait, an unvalidated crowdsourcing assumption, and an invented latent construct (advanced reasoning) with no independent measurement. Counts here reflect what is pulled from design choice rather than established evidence.

free parameters (3)
  • GRPO training hyperparameters (temperature=0.9, top-p=1.0, G=6, prompt/output token limits)
    Chosen by hand and shared across runs; standard for GRPO, but any of these could influence the robustness drop.
  • Regular-expression format rewards in Stages A and C
    Reward-shaping rules tuned to the paper's desired output format, not derived from data; they define what counts as a correctly formatted and well-reasoned answer.
  • Single-ground-truth sample filter
    Only samples with one explicit answer are kept across datasets, a post hoc selection that changes the composition of every benchmark.
assumptions (4)
  • domain assumption GRPO objective (Eq. 4) optimizes reasoning under the chosen reward scheme
    Borrowed from Guo et al. (2025) and treated as a working post-training method; its failure modes (e.g., sparse-reward collapse for Llama3.1) are invoked ad hoc to explain results.
  • ad hoc to paper Given that large models are pre-trained on extensive datasets, we assume they possess basic reasoning capabilities
    Section Robustness to Noise; the guiding example is claimed to activate these assumed latent skills, an unfalsifiable postulate used to interpret remediation gains.
  • ad hoc to paper Requiring per-option analysis in Stage D isolates summary-inference ability
    The StageAB-to-StageAD drop is attributed to advanced-reasoning deficits, but no control instruction of matched length and complexity is tested, so prompt-format sensitivity is a competing explanation.
  • domain assumption Crowdsourced noise preserves the original meaning of each question
    Section Constructing Noisy Evaluation Sets states two construction principles, but inter-annotator agreement and validation metrics are deferred to a missing appendix.
invented entities (1)
  • advanced reasoning capability
    purpose: Latent trait invoked to explain accuracy drops under the three scenarios and the recovery from StageCD/GH interventions
    Never measured independently of the evaluation prompts themselves; the very instruction change defines the deficit, so the construct has no falsifiable handle outside the paper's protocol.

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Pith. "Pith review of Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning." pith.science (2026). https://pith.science/paper/KETDJT24

@misc{pith2026250804848,
  author       = {Pith},
  title        = {Pith review of: Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KETDJT24}},
  note         = {Machine review of arXiv:2508.04848}
}
read the original abstract

Reinforcement learning (RL) has become a key technique for enhancing the reasoning abilities of large language models (LLMs), with policy-gradient algorithms dominating the post-training stage because of their efficiency and effectiveness. However, most existing benchmarks evaluate large-language-model reasoning under idealized settings, overlooking performance in realistic, non-ideal scenarios. We identify three representative non-ideal scenarios with practical relevance: summary inference, fine-grained noise suppression, and contextual filtering. We introduce a new research direction guided by brain-science findings that human reasoning remains reliable under imperfect inputs. We formally define and evaluate these challenging scenarios. We fine-tune three LLMs and a state-of-the-art large vision-language model (LVLM) using RL with a representative policy-gradient algorithm and then test their performance on eight public datasets. Our results reveal that while RL fine-tuning improves baseline reasoning under idealized settings, performance declines significantly across all three non-ideal scenarios, exposing critical limitations in advanced reasoning capabilities. Although we propose a scenario-specific remediation method, our results suggest current methods leave these reasoning deficits largely unresolved. This work highlights that the reasoning abilities of large models are often overstated and underscores the importance of evaluating models under non-ideal scenarios. The code and data will be released at XXXX.

Figures

Figures reproduced from arXiv: 2508.04848 by the authors.

Figure 1
Figure 1. Illustration of problem settings used to evaluate [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of the training and evaluation stages used to construct the RL-fine-tuned model and its variants. The reward [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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