{"id":"f285836d-7ed4-4fd0-b21f-98b2c9af6db0","arxiv_id":"2605.24001","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"DIDR aligns one-step diffusion generators by propagating reward-tilted distributions across noise levels, achieving better preference alignment than multi-step teachers in a single step.","lead":"The paper proposes Diff-Instruct with Diffused Reward (DIDR), a framework that spreads RLHF rewards across diffusion noise levels via integral KL minimization for one-step text-to-image generators. This could enable faster models that better match human preferences without the fidelity loss seen in prior one-step RL approaches.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Integral KL objective may fail to propagate reward-tilted distribution without bias from noise schedule or DRP approximation","rationale":"The reader's weakest_assumption directly isolates the internal condition required for the strongest_claim. Because the review was abstract-only, the derivation remains unchecked, but the concern is technical and falsifiable rather than external to the paper's logic.","tokens_in":1728,"tokens_out":306,"duration_ms":19773,"concrete_test":"For a 1D Gaussian diffusion toy model with quadratic reward, analytically minimize the integral KL objective over the full trajectory and compare the resulting distribution to the clean-image RLHF optimum; repeat for two different noise schedules (e.g., linear vs. cosine) and for DRP with 1 vs. 5 short steps. If the minimizer shifts with schedule or step count, the propagation claim fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim requires that the integral KL minimization objective propagates the RLHF-optimal reward-tilted clean-image distribution across all noise levels and shares the identical minimizer with clean-image RLHF. This holds only if the objective is free of bias induced by the specific noise schedule or by the short-step DRP estimator. The abstract provides no derivation showing schedule-independence or that the DRP approximation preserves the exact minimizer; any schedule dependence or approximation error would break the same-minimizer property and render DRS a biased correction rather than a faithful one.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes Diff-Instruct with Diffused Reward (DIDR), a data-free trajectory-level alignment method for one-step text-to-image generators derived from Integral KL minimization. It claims that DIDR propagates the RLHF-optimal reward-tilted clean-image distribution across all noise levels, admits the same minimizer as clean-image RLHF, induces a Diffused Reward Score (DRS) as a reward-driven correction to the reference score, and employs a practical Diffused Reward Proxy (DRP) estimator via short-step differentiable denoising. Experiments reportedly show consistent Pareto dominance over one-step SDXL baselines and, on a 6B DiT backbone, surpassing a 50-step teacher model in preference alignment with a single generation step.","tokens_in":1885,"tokens_out":572,"duration_ms":26297,"significance":"If the same-minimizer property and unbiased propagation of the reward-tilted distribution are rigorously established, the work would offer a principled alternative to existing reward-optimization approaches for one-step diffusion models, potentially resolving the mismatch between terminal rewards and generative dynamics while remaining data-free. The introduction of DRS as a correction term and the DRP estimator could influence trajectory-level RL methods in diffusion models more broadly.","major_comments":[{"comment":"Abstract: The central claim that the integral KL objective 'admits the same minimizer as clean-image RLHF' and propagates the reward-tilted distribution without bias is load-bearing for the 'principled' framing, yet the abstract (and available text) provides no derivation steps, schedule-independence proof, or error analysis for the DRP short-step approximation; this must be supplied explicitly, e.g., via an expanded §3 or appendix with the relevant integral and minimizer equivalence.","section":"Abstract"},{"comment":"Abstract: The claim that DIDR 'naturally inducing the Diffused Reward Score (DRS)' as an exact reward-driven correction rests on the integral KL construction, but no analysis is given showing that the practical DRP estimator (or any implicit fitting) preserves independence from the noise schedule; any schedule dependence would undermine the same-minimizer property and render DRS a biased correction.","section":"Abstract"}],"minor_comments":[{"comment":"The abstract introduces new terms (Diffused Reward Score, Diffused Reward Proxy) without immediate notational definitions or forward references to their formal definitions in the main text.","section":"Abstract"},{"comment":"Experimental claims of Pareto dominance and surpassing the teacher model would benefit from explicit controls or ablations addressing the DRP approximation error in the main results section.","section":null}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback emphasizing the need for explicit theoretical support of our central claims. We will revise the manuscript to strengthen the presentation of the derivations while preserving the core contributions.","responses":[{"response":"We agree that the abstract, being a concise summary, does not include full derivation steps. The integral KL minimization, same-minimizer equivalence to clean-image RLHF, and unbiased propagation of the reward-tilted distribution are derived in Section 3 of the manuscript. To address the comment directly, the revision will expand §3 with explicit step-by-step derivations of the integral objective and minimizer equivalence, add a schedule-independence proof, and include an appendix with error bounds and analysis for the DRP short-step approximation.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The central claim that the integral KL objective 'admits the same minimizer as clean-image RLHF' and propagates the reward-tilted distribution without bias is load-bearing for the 'principled' framing, yet the abstract (and available text) provides no derivation steps, schedule-independence proof, or error analysis for the DRP short-step approximation; this must be supplied explicitly, e.g., via an expanded §3 or appendix with the relevant integral and minimizer equivalence."},{"response":"The natural induction of DRS as a reward-driven correction follows from the integral KL construction detailed in Section 3. We acknowledge that explicit analysis of the DRP estimator's schedule independence is not fully elaborated. In the revision we will add a dedicated subsection analyzing the short-step differentiable denoising approximation, showing that it preserves the key independence properties (and thus the same-minimizer guarantee) under the conditions and schedules used in the experiments, while discussing any practical biases and mitigation strategies.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The claim that DIDR 'naturally inducing the Diffused Reward Score (DRS)' as an exact reward-driven correction rests on the integral KL construction, but no analysis is given showing that the practical DRP estimator (or any implicit fitting) preserves independence from the noise schedule; any schedule dependence would undermine the same-minimizer property and render DRS a biased correction."}],"tokens_in":1481,"tokens_out":487,"duration_ms":26841,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The new piece is the Diffused Reward Score derived from integral KL minimization, meant to push the reward-tilted clean distribution through the entire diffusion trajectory for one-step generators. This is positioned as different from prior one-step RL that mixes image reward with noisy distribution matching. The paper reports that the objective shares the minimizer with standard clean-image RLHF and that the practical DRP estimator lets them train without data.\n\nExperiments claim Pareto dominance over SDXL one-step baselines and, on a 6B DiT, better preference scores than the 50-step teacher in one step. Those results are the concrete part worth checking.\n\nThe soft spot is exactly the one the stress-test raises: the abstract gives no derivation showing that the integral KL is free of bias from the noise schedule or from the short-step DRP approximation. If either introduces error, the same-minimizer property does not hold and DRS becomes an approximate correction rather than an exact one. Without the full derivation or controls for schedule dependence, the central claim stays unverified.\n\nThis is for people working on real-time diffusion and RLHF alignment. The thinking engages the right problem and cites the relevant prior one-step methods. It deserves a serious referee to examine the math and the experimental controls, even if revisions are likely needed on the approximation details.","headline":"DIDR's integral KL construction for one-step alignment is a reasonable attempt at fixing reward-dynamics mismatch, but the same-minimizer claim rests on unshown steps that the stress-test flags correctly.","tokens_in":2347,"tokens_out":354,"would_cite":false,"duration_ms":16570,"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":"DIDR propagates the RLHF-optimal reward-tilted clean-image distribution across all noise levels to align one-step generators.","keywords":["one-step text-to-image generation","reinforcement learning","RLHF","diffusion models","distribution alignment","reward optimization","trajectory-level alignment"],"falsifier":"Train a model under DIDR and directly compare its zero-noise marginal distribution against the distribution obtained by optimizing the same reward on clean images only; systematic mismatch would falsify the propagation claim.","tokens_in":2646,"feed_emoji":"🖼️","tokens_out":682,"duration_ms":19058,"temperature":0.7,"pith_summary":"The paper introduces Diff-Instruct with Diffused Reward (DIDR) to fix the mismatch between terminal reward optimization and generative dynamics in one-step text-to-image models. It derives a data-free framework from integral KL minimization that spreads the reward-tilted distribution from clean images through the entire diffusion trajectory. This objective shares the same minimizer as direct clean-image RLHF and produces the Diffused Reward Score as a correction to the reference score. A practical Diffused Reward Proxy estimates the score via short-step denoising. Experiments show the resulting one-step models Pareto-dominate prior baselines and can exceed a 50-step teacher on preference alignment.","feed_headline":"Diffused reward aligns one-step generators to RLHF optima","feed_subtitle":"Integral KL minimization spreads the clean-image optimal distribution across noise levels, matching direct RLHF while enabling single-step i","key_machinery":"Integral KL minimization objective that propagates the reward-tilted distribution across the diffusion trajectory and induces the Diffused Reward Score (DRS) as a correction term.","core_discovery":"DIDR is a trajectory-level alignment method derived from integral KL minimization. It propagates the RLHF-optimal reward-tilted clean-image distribution across all noise levels along the diffusion trajectory. The objective admits the same minimizer as clean-image RLHF and naturally induces the Diffused Reward Score, which serves as a reward-driven correction to the reference score function. The Diffused Reward Proxy supplies an efficient estimator of this score through differentiable short-step denoising.","pith_inferences":["The same integral-KL propagation principle could be applied to other stochastic generative processes such as flow-matching models.","If the diffused correction works at every noise level, the approach may generalize to reward alignment for video or 3D generation without retraining the full trajectory.","The method suggests that explicit trajectory-level objectives can replace separate distribution-matching and reward stages in future one-step RL pipelines."],"forward_implications":["One-step generators achieve the identical optimal distribution as clean-image RLHF.","Optimization no longer trades image fidelity for higher reward by exploiting stochastic degrees of freedom.","The framework transfers to large DiT backbones and yields single-step models that surpass their multi-step teachers in preference alignment.","Consistent Pareto dominance holds over existing one-step SDXL baselines under the same reward signals."],"fun_headline_variants":["DIDR matches RLHF optima via diffused reward","Diffused reward propagates across diffusion trajectory","DRS corrects reference score for one-step RLHF","DRP enables efficient diffused reward estimation"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The integral KL minimization objective correctly propagates the reward-tilted distribution across the full diffusion trajectory without bias from the noise schedule or the short-step DRP estimator approximation.","fun_headline_variants_meta":{"raw":{"variants":["DIDR matches RLHF optima via diffused reward","Diffused reward propagates across diffusion trajectory","DRS corrects reference score for one-step RLHF","DRP enables efficient diffused reward estimation"]},"model":"grok-4.3","cost_usd":0.008771,"raw_usage":{"total_tokens":3965,"prompt_tokens":698,"num_sources_used":0,"completion_tokens":47,"cost_in_usd_ticks":87712000,"prompt_tokens_details":{"text_tokens":698,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3220,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":698,"tokens_out":47,"duration_ms":25350,"temperature":1.0,"reasoning_tokens":3220,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T18:13:01.228335+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Train a model under DIDR and directly compare its zero-noise marginal distribution against the distribution obtained by optimizing the same reward on clean images only; systematic mismatch would falsify the propagation claim.","supporting_citations":[],"review_version":1}