{"id":"553771e2-a925-45a2-989a-99a25c8cb590","arxiv_id":"2601.22664","paper_version":4,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"R2M is a new RLHF framework that uses policy hidden states for real-time alignment to mitigate reward overoptimization during policy distribution shifts.","lead":"The paper introduces R2M, a lightweight RLHF framework that aligns reward models using real-time feedback from the policy model's evolving hidden states rather than only semantic representations. A smart generalist might read it to understand a potential new way to reduce reward overoptimization when training large language models with human feedback.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest assumption directly names the load-bearing empirical premise. No stronger internal inconsistency or hidden assumption appears in the provided abstract-level description. The verdict therefore remains UNVERDICTED pending access to the full methods and results sections.","tokens_in":1669,"tokens_out":310,"duration_ms":33110,"concrete_test":"Re-run the RL training loop with an ablated R2M variant that receives only the semantic encoder output (no policy hidden states) and measure the reward discrepancy curve and final win-rate; if the gap between full R2M and ablated version is statistically insignificant across three random seeds, the added value of the policy-feedback channel is not demonstrated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that policy hidden states supply a real-time signal of distribution shift that lets the reward model align beyond static semantic representations and thereby reduce reward discrepancy. For this to hold, the hidden-state feedback must (a) encode policy-specific distributional information not already present in the semantic encoder and (b) be usable by a lightweight update rule that actually shrinks the observed reward gap during RL. The abstract and reader summary present this as a promising direction without internal contradictions or unsupported leaps; the mechanism is described at a high level but is not shown to rest on an obviously false premise. Because the full manuscript was not supplied in the query, no concrete flaw in equations, experimental design, or derivation can be isolated.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces R2M, a lightweight RLHF framework that aligns the reward model in real time by leveraging evolving hidden states from the policy model (termed 'policy feedback') to address distribution shifts during RL training. This is positioned as going beyond prior approaches that rely only on surface semantic representations from pretrained LLMs, with the goal of reducing reward discrepancy and mitigating overoptimization.","tokens_in":1807,"tokens_out":358,"duration_ms":68814,"significance":"If validated, the approach could open a useful direction for dynamic reward alignment in RLHF by incorporating policy-specific signals from hidden states. The lightweight framing is a practical strength, and the focus on real-time adaptation to policy changes addresses a known issue in current RLHF pipelines. However, without any empirical results, derivations, or experiments shown, the significance remains prospective rather than demonstrated.","major_comments":[{"comment":"Abstract: The central claim that policy hidden states supply a real-time signal of distribution shift that reduces reward discrepancy (beyond semantics) is presented without any supporting experiments, quantitative results, or ablation studies. This leaves the key assumption—that hidden states encode useful policy-specific distributional information not already captured by the semantic encoder—unexamined and load-bearing for the proposal.","section":"Abstract"}],"minor_comments":[{"comment":"Abstract: Grammatical issue in 'Prior mitigations primarily relies on surface semantic information' (should be 'rely').","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The manuscript reads as an early-stage proposal; the journal may wish to assess whether it prefers fully validated methods or is open to promising but unevaluated frameworks in this area."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback on our manuscript. We address the major comment below and commit to revisions that strengthen the empirical grounding of our proposal.","responses":[{"response":"We acknowledge that the current manuscript presents R2M primarily as a conceptual framework and does not include empirical results, quantitative evaluations, or ablation studies to directly validate the assumption that policy hidden states encode distributional information beyond what is captured by semantic encoders. The work is intended to highlight a promising direction for real-time alignment rather than to fully demonstrate it. In the revised version, we will add preliminary experiments on a controlled RLHF setup, including quantitative comparisons of reward discrepancy with and without policy feedback, as well as ablations that isolate the contribution of hidden-state signals versus semantic representations alone. These additions will directly address the load-bearing assumption.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The central claim that policy hidden states supply a real-time signal of distribution shift that reduces reward discrepancy (beyond semantics) is presented without any supporting experiments, quantitative results, or ablation studies. This leaves the key assumption—that hidden states encode useful policy-specific distributional information not already captured by the semantic encoder—unexamined and load-bearing for the proposal."}],"tokens_in":1238,"tokens_out":274,"duration_ms":38838,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this paper introduces R2M as a lightweight framework that pulls in the policy model's evolving hidden states to keep the reward model aligned during RL training. It targets the gap that opens when the policy shifts away from the original distribution, something static semantic checks do not catch well enough. That is the actual new angle here, and it is a reasonable way to frame the overoptimization problem in RLHF. The motivation section does a clean job explaining why prior surface-level fixes fall short and why real-time policy feedback could matter. The writing stays focused on the practical issue without overclaiming results. What the paper does well is identify a concrete failure mode in current alignment pipelines and sketch a direction that uses internal model states rather than just output text. That idea has some intuitive appeal for anyone who has watched reward scores climb while actual behavior drifts. The soft spots are straightforward. The abstract and summary contain no experiments, no ablation on whether the hidden-state signal adds anything beyond what a semantic encoder already sees, and no description of the update rule or its cost. Without those pieces it is impossible to tell if the approach actually shrinks the reward discrepancy or just adds another moving part. The central assumption that policy hidden states carry usable distributional information not already captured elsewhere is left untested. This paper is aimed at people working on RLHF implementations who want ideas for tightening the reward-policy loop. A reader already familiar with the overoptimization literature would see the proposal as a natural next step to explore, even if the current version is mostly a high-level sketch. I would send it to peer review. The topic is relevant and the framing is honest; a full version with even modest experiments would give referees something concrete to evaluate.","headline":"R2M proposes using policy hidden states for real-time reward alignment in RLHF to counter distribution shifts, but the abstract gives no evidence it works.","tokens_in":2291,"tokens_out":419,"would_cite":false,"duration_ms":22473,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"R2M leverages the evolving hidden states of the policy ... to align with the real-time distribution shift ... beyond surface semantic information."},{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/ArithmeticFromLogic.lean","rs_theorem":"LogicNat.induction","paper_passage":"Theorem 3.1 ... ϵ_R2M ≤ (1−γ(t))^{1/2}·C + ΔD(t)·L"}],"headline":"Standard RLHF reward-model update; no RS-shaped cost, ratio symmetry or distinction forcing","alignment":"orthogonal","rationale":"The paper's machinery (policy-hidden-state cross-attention, time-step weighted RTE fusion, GREBT loss with entropy regularisation, and the two misalignment bounds in Theorems 3.1/4.1) operates entirely inside contemporary RLHF/LLM training. It never invokes a reciprocal cost J, golden-ratio fixed points, 8-tick periodicity, or any parameter-free derivation from a single distinction. The RS corpus (AbsoluteFloorClosure, Cost.FunctionalEquation, AlexanderDuality, etc.) therefore supplies neither confirmation nor contradiction.","tokens_in":61726,"confidence":"high","tokens_out":321,"duration_ms":11760,"cache_read_input_tokens":32896,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Real-Time Aligned Reward Model uses policy hidden states to align with real-time distribution shifts beyond semantics.","keywords":["RLHF","reward model","reward overoptimization","policy feedback","hidden states","distribution shift","LLM alignment","real-time alignment"],"falsifier":"A controlled RLHF run in which R2M produces no measurable reduction in reward discrepancy or no gain in downstream alignment metrics relative to a standard semantic reward model would falsify the central claim.","tokens_in":2589,"feed_emoji":"🧠","tokens_out":575,"duration_ms":59440,"temperature":0.7,"pith_summary":"The paper introduces R2M as a lightweight framework for RLHF that addresses reward overoptimization by adapting the reward model to ongoing changes in the policy model. It does this by drawing on the evolving hidden states of the policy rather than depending only on semantic representations from a pretrained model. This matters because policy distribution shifts during training create growing misalignment that standard approaches miss, causing the reward model to reward spurious patterns instead of true human preferences. If the approach holds, reward discrepancy decreases and the policy model learns more faithfully from human feedback.","feed_headline":"Policy hidden states align reward models in real time","feed_subtitle":"Using evolving internal states from the policy reduces misalignment from distribution shifts during RLHF training.","key_machinery":"The Real-Time Aligned Reward Model (R2M) that incorporates policy feedback from the evolving hidden states of the policy model to dynamically adjust to distribution shifts.","core_discovery":"R2M leverages the evolving hidden states of the policy to align with the real-time distribution shift of the policy during the RL process, addressing misalignment caused by continuous policy changes beyond surface semantic information and thereby reducing reward discrepancy.","pith_inferences":["The same real-time feedback mechanism could apply to reinforcement learning settings outside language models.","Internal representations may carry alignment-relevant information that output tokens alone do not reveal.","Combining this approach with other dynamic alignment techniques could further reduce the need for frequent reward model retraining."],"forward_implications":["Reward overoptimization decreases because the reward model tracks policy changes in real time.","The policy model captures human intent more accurately by avoiding exploitation of spurious reward patterns.","Reward models achieve better performance through direct use of policy feedback instead of static semantic features.","RLHF training becomes more stable as misalignment from continuous distribution shifts is mitigated."],"fun_headline_variants":["Real time policy states align reward models","R2M aligns rewards with evolving policy states","Hidden states fix reward misalignment in real time","Policy feedback aligns reward models beyond semantics"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The hidden states from the policy model provide sufficient and useful signal for real-time alignment of the reward model that goes beyond semantic representations and effectively reduces reward discrepancy during training.","fun_headline_variants_meta":{"raw":{"variants":["Real time policy states align reward models","R2M aligns rewards with evolving policy states","Hidden states fix reward misalignment in real time","Policy feedback aligns reward models beyond semantics"]},"model":"grok-4.3","cost_usd":0.011739,"raw_usage":{"total_tokens":5017,"prompt_tokens":589,"num_sources_used":0,"completion_tokens":52,"cost_in_usd_ticks":117390500,"prompt_tokens_details":{"text_tokens":589,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":4376,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":589,"tokens_out":52,"duration_ms":58525,"temperature":1.0,"reasoning_tokens":4376,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-21T15:06:56.580205+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled RLHF run in which R2M produces no measurable reduction in reward discrepancy or no gain in downstream alignment metrics relative to a standard semantic reward model would falsify the central claim.","supporting_citations":[],"review_version":1}