{"id":"d269129b-496e-4557-ab18-5041e34987bf","arxiv_id":"2508.02260","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"In RLVR, entropy reduction in negative samples drives early gains, and later learning depends on high-entropy tokens in low-perplexity samples and at sequence ends, leading to two reward-shaping methods.","lead":"This paper analyzes how policy entropy changes during reinforcement learning with verifiable rewards, splitting training into rising and plateau stages and identifying which tokens most improve learning. It proposes two reward-adjustment methods based on perplexity and token position, reporting improvements across several language models.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Causal interpretation of token-level entropy correlations is the load-bearing step; without an ablation, the proposed reward adjustments may succeed for reasons unrelated to the claimed mechanism.","rationale":"The reader's weakest assumption concerned the stability and meaningfulness of the two-stage entropy division. I agree that stage-boundary arbitrariness is a concern, but the more load-bearing assumption in the central claim is the causal step from token-level correlations to reward-modification effectiveness. Even if the stage boundaries are perfectly stable, the paper's proposed methods depend on the claim that high-entropy tokens in low-perplexity, end-of-sequence positions are the causal drivers of learning efficiency. The abstract does not report ablations that would rule out confounding by position or perplexity alone. The reader's verdict was UNVERDICTED due to abstract-only review; my concern reinforces that. Since I cannot examine the full text, I do not move the verdict to REJECT, but I also do not find independent support for the causal claim. The concrete test proposed would settle the causal concern. Therefore UNVERDICTED remains appropriate, with the caveat that the test is essential for any future ACCEPT/CONDITIONAL recommendation. I partially agree with the reader because they focused on stage stability, which is a different but related weakness; my concern is the inference from correlation to causation, which the reader did not explicitly identify.","tokens_in":710,"tokens_out":1685,"duration_ms":24024,"concrete_test":"Run an ablation on the primary RLVR benchmark and model reported in the paper: keep the perplexity-based and position-based reward adjustments exactly as proposed, but replace the entropy criterion for token selection with random token selection matched to the same marginal distribution of token positions and sample perplexities. Repeat with 3 seeds and report mean and standard error. If the random-token version matches the reported improvements within error, the entropy mechanism is not causal and the central claim fails; if the entropy-based version significantly outperforms the random-token control, the causal role is supported.","verdict_should_be":"UNVERDICTED","load_bearing_attack":"The central claim has two parts: a descriptive two-stage decomposition of entropy-performance exchange, and a prescriptive claim that high-entropy tokens in low-perplexity samples and at sequence ends drive learning efficiency in the plateau stage, motivating reward adjustments. The abstract provides only correlational evidence for the prescriptive part. The proposed methods dynamically modify rewards using perplexity and positional information, but the abstract does not report ablations that isolate the contribution of the entropy signal. If the observed correlation is confounded by token position, sample length, or the RL algorithm's intrinsic exploration schedule, the reward adjustments may be effective merely as heuristics that upweight certain positions or low-perplexity samples, not because of any causal role of token entropy. This is load-bearing because the paper's stated contribution is that the entropy-based mechanism, not the auxiliary proxies, unlocks RLVR gains. Without a test that removes the entropy channel while keeping the proxies, the central claim is not established.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper studies the exchange between policy entropy and performance in reinforcement learning with verifiable rewards (RLVR). It proposes dividing the training process into a rising stage and a plateau stage based on entropy dynamics, then analyzes how entropy-performance relationships differ across stage-level, instance-level, and token-level granularities. The abstract claims that in the rising stage, entropy reduction in negative samples facilitates learning of effective reasoning patterns, while in the plateau stage, learning efficiency strongly correlates with high-entropy tokens in low-perplexity samples and at sequence ends. Based on these findings, the authors propose two reward-adjustment methods that use perplexity and positional information to focus RL updates on tokens with high learning potential, reporting improvements over baselines on various LLMs. The full text was not available for this review.","tokens_in":886,"tokens_out":3743,"duration_ms":40218,"significance":"If the two-stage decomposition and the token-level correlations are validated, this work could offer a practical, interpretable basis for reward shaping in RLVR, with potentially broad applicability across LLM reasoning tasks. The proposed methods are conceptually simple and computationally light, which is a strength if they genuinely outperform baselines. However, the abstract alone provides only correlational evidence and gives no details about ablations, statistical rigor, or the separation between analysis and evaluation data. The mechanistic claims, especially the causal role of token entropy in the plateau stage, are the central contribution and need strong empirical support; the abstract does not currently establish that support.","major_comments":[{"comment":"The prescriptive claim that in the plateau stage learning efficiency is driven by high-entropy tokens in low-perplexity samples and at sequence ends is supported only by correlational evidence; the abstract reports no ablation that removes the entropy channel while retaining the positional and perplexity proxies. This is load-bearing because the proposed reward adjustments are presented as consequences of an entropy-based mechanism. Without such an ablation, the improvements could arise from upweighting sequence-end positions or low-perplexity samples irrespective of token entropy. Please provide an ablation that isolates the entropy signal or explicitly address the confounding.","section":"Abstract (token-level analysis)"},{"comment":"The two-stage division based on entropy dynamics is a foundational step for the entire analysis, but the abstract gives no information about how the boundary between rising and plateau stages is determined, nor whether this boundary is stable across datasets, model scales, or runs. If the boundary is chosen post hoc from the same data, the token-level correlations and the resulting reward adjustments may not generalize to new settings. The authors should specify the stage-division rule and report sensitivity analyses over seeds and hyperparameters.","section":"Abstract (stage division)"},{"comment":"There is a risk of circularity because the proposed reward adjustments are directly motivated by the same analysis that discovered the entropy-performance correlations. The abstract does not state whether the training runs used for the analysis are the same as those used for evaluating the adjustments, nor whether the adjustment weights are selected post hoc. Please clarify the separation between analysis and evaluation data and the procedure for choosing the adjustment hyperparameters.","section":"Abstract (method validation)"},{"comment":"The abstract states improvements over baselines on various LLMs but provides no quantitative detail or uncertainty estimates. Given that the analysis is empirical and the proposed methods involve multiple hyperparameters, the abstract should at least mention the scale of the improvements and the statistical variability across seeds. Without this, the robustness of the reported gains cannot be assessed.","section":"Abstract (evaluation)"}],"minor_comments":[{"comment":"The word 'granularitiess' in the sentence 'across stage-level, instance-level, and token-level granularitiess' is a typo and should be 'granularities'.","section":"Abstract"},{"comment":"The term 'negative samples' is used without definition; please clarify whether it refers to trajectories with low verifiable reward or to tokens that reduce entropy, as this affects the interpretation of the rising-stage claim.","section":"Abstract"},{"comment":"The abstract does not specify the exact reward-adjustment mechanism (e.g., whether the adjustments are multiplicative or additive, or how perplexity is computed). A concise description would improve clarity.","section":"Abstract"}],"recommendation":"uncertain","confidential_remarks":"The manuscript was reviewed on the basis of the abstract alone because the full text was not provided. The major comments reflect concerns that may already be addressed in the full manuscript, but the abstract does not supply the necessary evidence. I recommend soliciting the full text before making an editorial decision; the 'uncertain' verdict is a consequence of this incomplete review rather than a negative assessment of the underlying work."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague—\n\nThe abstract is doing more work than most: it actually tells you what the paper found. Two-stage entropy dynamics in RLVR, and in the plateau stage a token-level correlation between high-entropy tokens (in low-perplexity samples and at sequence ends) and learning efficiency. That granularity is new, as far as I can tell, and the practical payoff—two reward-adjustment methods using perplexity and position—is concrete and testable.\n\nWhat the paper does well: it doesn't just report a loss curve; it tries to decompose where learning happens, which is the kind of analysis the RLVR field needs. The proposed methods are simple enough to implement and the claim of improvements over baselines across various LLMs is at least a falsifiable assertion.\n\nThe soft spot is causal. The abstract gives correlational evidence for the token-level story, and the proposed reward adjustments could be effective simply because they upweight certain positions or downweight high-perplexity samples, with entropy being an epiphenomenon. The stage division is a modeling choice—thresholds matter—and the improvement numbers come from a method motivated by the same analysis, so circularity is a real risk unless the full text reports ablations that remove the entropy channel while keeping the proxies, plus out-of-sample evaluations. I don't see that in the abstract. The stress-test note is right: this is the load-bearing step.\n\nI'm not treating any of this as fatal. The descriptive findings may stand regardless of the causal interpretation. But the paper's headline claim—that the entropy mechanism, not the proxies, unlocks the gains—is not established by what's shown here.\n\nWho this is for: anyone working on RL for LLM reasoning, especially people designing reward-shaping heuristics. If the full text has the ablations and a clean stage-boundary robustness check, it deserves careful reading. Without them, it's a promising empirical report with an overreaching title.\n\nRecommendation: send it to peer review. A serious referee should push for the ablation and for sensitivity analysis on the stage thresholds and reward weights. The question is interesting enough that the field should see the details, not a desk rejection.","headline":"A useful empirical decomposition of RLVR entropy dynamics, but the causal story needs ablations the abstract doesn't report.","tokens_in":1341,"tokens_out":1958,"would_cite":false,"duration_ms":23149,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"RLVR's entropy-performance exchange has two stages, and late-stage learning concentrates in high-entropy tokens from easy samples and sequence endings.","keywords":["reinforcement learning with verifiable rewards","entropy-performance exchange","large language models","reasoning","reward shaping","perplexity","token-level analysis","policy entropy"],"falsifier":"Run RLVR on a new model and task, and during the plateau stage compute per-token learning efficiency (for example, the reduction in policy loss attributable to updating on each token). If high-entropy tokens in low-perplexity samples and at sequence ends do not account for most of the remaining learning, or if the entropy curve does not show a clear rising-then-flat shape, the stage decomposition and the reward-adjustment methods lose their empirical basis.","tokens_in":550,"feed_emoji":"🎯","tokens_out":6159,"duration_ms":64371,"temperature":0.7,"pith_summary":"This paper tries to establish that the exchange between policy entropy and performance during reinforcement learning with verifiable rewards (RLVR) is a two-stage process, and that the stage of apparent saturation hides a token-level structure worth exploiting. The authors argue that in the rising stage, entropy reduction in negative samples is what drives rapid reasoning gains, while in the plateau stage, the updates that still matter are concentrated in high-entropy tokens located in low-perplexity samples and at the end of sequences. From this they derive two reward-adjustment methods, one based on sample perplexity and one on token position, that redirect updates toward those high-potential tokens, and they report improvements over baseline RLVR on multiple LLMs. If the claim is right, RLVR practitioners can improve reasoning performance without changing the underlying algorithm, simply by reweighting rewards based on where learning is actually happening.","feed_headline":"Late-stage RL gains ride on high-entropy tokens in easy samples","feed_subtitle":"Reward shaping by perplexity and token position improves reasoning across several LLMs.","key_machinery":"The central machinery is a granularity decomposition of the RLVR training process: the training run is first split into a rising stage and a plateau stage by the dynamics of policy entropy (the spread of the model's output distribution), and then learning efficiency is examined at the level of instances and of individual tokens. The analysis identifies which parts of the data carry learning in each stage, and that identification is the lever behind the two proposed reward-adjustment methods, which rescale rewards by sample perplexity and by token position so that updates concentrate on high-entropy tokens in low-perplexity samples and at sequence ends.","core_discovery":"The central discovery is that the entropy-performance exchange in RLVR is not a uniform trade-off but a two-stage process. During the rising stage, the policy's entropy grows and entropy reduction in negative samples is what allows the model to pick up effective reasoning patterns, producing rapid performance gains. During the plateau stage, aggregate entropy has stabilised, yet learning is still happening unevenly at the token level: the tokens with the highest learning potential are high-entropy tokens found in low-perplexity samples and tokens located at the end of sequences. The authors turn this into two reward-adjustment methods, one using sample perplexity and one using token position, that reweight the reward signal to concentrate updates on these high-potential tokens, and they report consistent improvements over baseline RLVR across several LLMs.","pith_inferences":["Editorial inference: the same perplexity-position reweighting could be tested in other RLVR domains, such as code generation or tool use, where the entropy-performance exchange may follow a similar two-stage shape.","Editorial inference: the stage boundary itself suggests a curriculum-style training rule, switching reward shaping on when the entropy curve plateaus, which would make the proposed methods adaptive rather than static.","Editorial inference: concentrating updates on high-entropy tokens in low-perplexity samples may amplify reward hacking if the model learns to game those tokens; a held-out evaluation on genuinely new prompts would test whether the gains are real reasoning improvements."],"forward_implications":["The two-stage picture predicts that reweighting RLVR rewards by perplexity and position should improve reasoning performance on any LLM exhibiting the same entropy dynamics, not just the models tested.","During the rising stage, training signals focused on negative samples should accelerate early performance gains, while during the plateau stage, signals focused on high-entropy tokens in low-perplexity samples and sequence ends should extend the useful part of training.","The proposed reward-adjustment methods are a direct, low-cost alternative or complement to modifying the policy objective itself, since they only rescale the reward signal.","If the token-level correlation is robust, then entropy flattening at the aggregate level should no longer be read as a sign that training is saturated; useful updates are still available in specific token subsets."],"supporting_citations":[],"fun_headline_variants":["Late-stage RL gains tie to high-entropy tokens in easy samples","Reward reweighting by perplexity and position improves RL reasoning","Two-stage entropy-performance exchange: token-level focus pays off","Plateau-stage learning key: high-entropy tokens in low-perplexity samples"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The claim depends on the entropy-based split into rising and plateau stages being a stable property of RLVR training rather than an artifact of particular runs, models, or tasks.","fun_headline_variants_meta":{"raw":{"variants":["Late-stage RL gains tie to high-entropy tokens in easy samples","Reward reweighting by perplexity and position improves RL reasoning","Two-stage entropy-performance exchange: token-level focus pays off","Plateau-stage learning key: high-entropy tokens in low-perplexity samples"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000212,"raw_usage":{"total_tokens":1410,"prompt_tokens":926,"completion_tokens":484,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":542,"completion_tokens_details":{"reasoning_tokens":408}},"tokens_in":542,"tokens_out":484,"duration_ms":5381,"temperature":1.0,"reasoning_tokens":408,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T05:01:40.915933+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run RLVR on a new model and task, and during the plateau stage compute per-token learning efficiency (for example, the reduction in policy loss attributable to updating on each token). If high-entropy tokens in low-perplexity samples and at sequence ends do not account for most of the remaining learning, or if the entropy curve does not show a clear rising-then-flat shape, the stage decomposition and the reward-adjustment methods lose their empirical basis.","supporting_citations":[],"review_version":1}