{"id":"98cfc703-cadd-4fe7-acbf-23ae4149f9f2","arxiv_id":"2606.08708","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"PRPO introduces Robust Visual Dependency (RVD) to identify pivotal perceptual tokens and Perceptual Advantage Reshaping (PAR) for token-level credit assignment in RLVR, reporting 21-23% average gains on multimodal reasoning benchmarks for 3B and 7B LVLMs.","lead":"PRPO is a reinforcement learning method for large vision-language models that identifies visually grounded tokens using a Robust Visual Dependency metric and reshapes token-level advantages to reinforce them. A smart generalist might read it because it targets a core problem in training AI to reason from images rather than language patterns alone.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"RVD's perturbation-stability criterion may conflate language-prior stability with true visual grounding, undermining the causal attribution of gains to perceptual token reinforcement.","rationale":"The reader's weakest_assumption directly names the RVD reliability issue as load-bearing; the abstract provides no additional evidence (such as ablation tables or causal probes) that would resolve it, so the UNVERDICTED status and low confidence remain appropriate.","tokens_in":1799,"tokens_out":338,"duration_ms":10408,"concrete_test":"Replace RVD token selection with a language-only baseline (e.g., tokens with highest next-token probability under a text-only forward pass) and rerun the 3B/7B training; if the average benchmark gains fall below 5% relative to the original PRPO, the visual-specificity of RVD is necessary for the reported improvements.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline performance claims (23.3%/21.1% gains, SOTA) rest on PAR amplifying tokens selected by RVD. RVD is defined as identifying tokens that are 'visually grounded and perturbation-stable.' If stability is measured via input perturbations that do not isolate visual evidence (e.g., generic noise rather than targeted visual masking or feature ablation), then RVD can select tokens whose predictions are stable due to strong textual priors or template patterns. Amplifying those tokens would then improve performance for reasons orthogonal to multimodal grounding, making the central mechanism unproven. No independent verification (gradient-based visual sensitivity, controlled visual ablation, or human grounding labels) is described that would falsify this alternative explanation.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes Perception-Reinforced Policy Optimization (PRPO), a token-level RLVR framework for LVLMs. It introduces Robust Visual Dependency (RVD) to identify tokens whose predictions are visually grounded and perturbation-stable, and Perceptual Advantage Reshaping (PAR) to amplify advantages for those tokens while preserving gradients for others. The central empirical claim is that PRPO yields average gains of 23.3% (3B) and 21.1% (7B) over strong baselines across seven multimodal reasoning benchmarks, reaching SOTA with improved efficiency and cross-task generalization.","tokens_in":1956,"tokens_out":505,"duration_ms":14546,"significance":"If the mechanism is shown to isolate visual grounding rather than language priors, the work would meaningfully advance fine-grained credit assignment in multimodal RL, addressing a clear mismatch between trajectory-level rewards and sparse perceptual tokens. The scale of reported gains and the explicit focus on token-level reshaping would be notable contributions if supported by targeted verification.","major_comments":[{"comment":"§3 (Method, RVD definition): The perturbation-stability criterion used to define RVD is not shown to isolate visual evidence; if the perturbations are generic noise rather than targeted visual masking or feature ablation, RVD can select tokens stable due to textual priors or templates. This directly undermines the causal attribution that PAR's amplification produces the reported gains via perceptual reinforcement rather than orthogonal factors.","section":"§3"},{"comment":"§4 (Experiments): No ablation is reported that isolates the contribution of RVD-selected tokens versus random or language-prior tokens under PAR, nor any controlled visual ablation study (gradient sensitivity, human grounding labels) to falsify the alternative that gains arise from non-perceptual mechanisms. Without this, the headline 23.3%/21.1% gains and SOTA claim rest on an unverified assumption.","section":"§4"}],"minor_comments":[{"comment":"The abstract states 'improved training efficiency' but provides no wall-clock, token-throughput, or convergence-curve comparisons to baselines; this should be quantified in §4 if claimed.","section":null}],"recommendation":"major_revision","confidential_remarks":"The manuscript is entirely empirical with no code or implementation details released in the provided text; this raises reproducibility concerns for a method whose core contribution is a new metric and reshaping procedure."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback on our work. We address each major comment below with targeted responses and indicate planned revisions to strengthen the validation of RVD's perceptual isolation and the causal role of PAR.","responses":[{"response":"We agree that stronger causal isolation of visual grounding is desirable. RVD is defined in §3 as the intersection of visual feature dependency (via gradient-based sensitivity to image regions) and stability under image perturbations. The perturbations are indeed generic noise as implemented, which tests robustness but does not fully rule out language-template effects. We will revise the method section to explicitly discuss this limitation and add a new analysis comparing RVD-selected tokens against those identified by a text-only variant of the model to demonstrate differential behavior attributable to visual input.","revision_made":"partial","referee_comment":"[§3] §3 (Method, RVD definition): The perturbation-stability criterion used to define RVD is not shown to isolate visual evidence; if the perturbations are generic noise rather than targeted visual masking or feature ablation, RVD can select tokens stable due to textual priors or templates. This directly undermines the causal attribution that PAR's amplification produces the reported gains via perceptual reinforcement rather than orthogonal factors."},{"response":"The current experiments compare PRPO against trajectory-level RLVR baselines and report consistent gains, but we acknowledge the absence of the specific ablations requested. We will add in the revised §4: (i) an ablation applying PAR to random tokens and to language-prior tokens (selected via a language-only model) for direct comparison, and (ii) gradient sensitivity analysis measuring visual feature influence on RVD tokens versus others. Human grounding labels are not part of our experimental setup or available datasets, so we cannot provide them; we will note this as a limitation and suggest it for future work.","revision_made":"yes","referee_comment":"[§4] §4 (Experiments): No ablation is reported that isolates the contribution of RVD-selected tokens versus random or language-prior tokens under PAR, nor any controlled visual ablation study (gradient sensitivity, human grounding labels) to falsify the alternative that gains arise from non-perceptual mechanisms. Without this, the headline 23.3%/21.1% gains and SOTA claim rest on an unverified assumption."}],"tokens_in":1487,"tokens_out":500,"duration_ms":21172,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main move is to replace trajectory-level rewards in RLVR with token-level signals that boost only the tokens RVD flags as visually grounded and perturbation-stable, then reshape advantages via PAR. That is new relative to the standard outcome-reward setups referenced in the abstract.\n\nIt does a clean job naming the mismatch: most tokens in a multimodal chain are not causally tied to the image, so uniform credit assignment lets language priors dominate. The reported average gains of 23% on 3B models and 21% on 7B across seven benchmarks are large enough that, if they hold under scrutiny, the method would be worth testing.\n\nThe soft spot is the one the stress-test flags. RVD selects tokens whose predictions survive perturbation, but the abstract gives no detail on what kind of perturbation is used or whether it targets visual features specifically. If the perturbations are generic noise, then tokens that stay stable because of strong textual templates or priors will be selected and amplified. That would produce the observed gains for reasons unrelated to perceptual reinforcement. No ablation, gradient map, or controlled visual masking is mentioned that would rule this out.\n\nThe work is aimed at groups already running RL on LVLMs and looking for finer credit assignment. A reader who wants concrete ideas for token-level shaping will find usable pieces even if the causal story needs more checks. It is coherent enough on its own terms to deserve a serious referee, mainly so that the RVD computation and perturbation protocol can be examined directly.\n\nI would send it to review rather than desk reject.","headline":"PRPO tries to fix coarse credit assignment in LVLM RL with a new token-level metric and reshaping step, but the gains rest on an unverified claim that RVD actually isolates visual grounding rather than language stability.","tokens_in":2461,"tokens_out":407,"would_cite":false,"duration_ms":15175,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Token-level credit assignment for visually grounded tokens lifts LVLM reasoning performance by over 20 percent on average.","keywords":["reinforcement learning","large vision-language models","token-level credit assignment","multimodal reasoning","visual grounding","policy optimization","advantage reshaping"],"falsifier":"A controlled experiment in which PRPO is applied to a model trained only on language priors with no visual input; if performance still rises, the claim that RVD isolates visual grounding collapses.","tokens_in":2698,"feed_emoji":"👁️","tokens_out":623,"duration_ms":11940,"temperature":0.7,"pith_summary":"The paper claims that standard reinforcement learning for vision-language models applies the same reward signal to every token in a response, even though only a few tokens actually depend on the image. This mismatch lets language-only patterns dominate training. PRPO fixes the mismatch by measuring which tokens stay stable when the image is perturbed and then boosting the learning signal only for those tokens. The result is stronger multimodal reasoning at both 3B and 7B scales across seven benchmarks, together with faster training and better transfer to new tasks.","feed_headline":"Token-level visual rewards raise LVLM reasoning scores 21-23 percent","feed_subtitle":"PRPO measures which tokens stay stable under image changes and amplifies their learning signal while leaving the rest untouched.","key_machinery":"Robust Visual Dependency (RVD) metric combined with Perceptual Advantage Reshaping (PAR) that amplifies advantages only for tokens that are both visually grounded and perturbation-stable.","core_discovery":"PRPO identifies tokens whose next-token predictions remain consistent under visual perturbations and then rescales their advantages upward while leaving non-visual tokens unchanged; the resulting policy updates produce consistent gains of 23.3 percent on 3B models and 21.1 percent on 7B models relative to strong trajectory-level baselines.","pith_inferences":["The same token-level filtering could be applied to pure language reasoning by replacing image perturbations with prompt perturbations.","If RVD proves reliable, it could serve as a diagnostic for when a model is actually using the image versus reciting memorized text.","Extending PAR to other reward sources, such as human preference scores, might reduce reward hacking in non-verifiable settings."],"forward_implications":["Average accuracy on seven multimodal reasoning benchmarks rises by 23.3 percent for 3B models and 21.1 percent for 7B models.","Training converges with fewer gradient steps than trajectory-level RLVR baselines.","The same policy transfers more effectively to unseen multimodal tasks.","Non-perceptual tokens retain stable gradient magnitudes, avoiding collapse of language-only capabilities."],"fun_headline_variants":["Token stability under image changes drives PRPO advantage scaling","PRPO rescales advantages for consistent visual prediction tokens","Perceptual advantage reshaping targets visual dependency in PRPO","PRPO applies token-level reshaping to robust perceptual tokens only"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The assumption that tokens whose predictions survive image perturbation are the ones causally responsible for correct answers rather than merely correlated with them.","fun_headline_variants_meta":{"raw":{"variants":["Token stability under image changes drives PRPO advantage scaling","PRPO rescales advantages for consistent visual prediction tokens","Perceptual advantage reshaping targets visual dependency in PRPO","PRPO applies token-level reshaping to robust perceptual tokens only"]},"model":"grok-4.3","cost_usd":0.005046,"raw_usage":{"total_tokens":2482,"prompt_tokens":712,"num_sources_used":0,"completion_tokens":62,"cost_in_usd_ticks":50462000,"prompt_tokens_details":{"text_tokens":712,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1708,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":712,"tokens_out":62,"duration_ms":13194,"temperature":1.0,"reasoning_tokens":1708,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T18:23:28.964583+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled experiment in which PRPO is applied to a model trained only on language priors with no visual input; if performance still rises, the claim that RVD isolates visual grounding collapses.","supporting_citations":[],"review_version":1}