{"id":"48035b2a-419c-46da-ba73-40dff1866b41","arxiv_id":"2601.22823","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"SCIQL is an offline RL algorithm that learns style-conditioned policies via hindsight relabeling, implicit Q-learning, and gated advantage weighting, and outperforms prior methods on style alignment and task performance.","lead":"This paper proposes SCIQL, an offline reinforcement learning method that trains a policy to adopt a user-defined behavior style (e.g., 'crawling' or 'fast') while still completing a task, using only pre-recorded data. It combines Implicit Q-Learning with hindsight relabeling and a gated advantage weighting rule, and reports better style alignment and task performance than prior baselines on 2D, robot, and humanoid benchmarks.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Surrogate Markov style reward is assumed, not proven, to optimize the true non-Markovian style objective; this gap is load-bearing for the style-alignment claim.","rationale":"The reader's weakest assumption is exactly the surrogate style reward gap. My analysis confirms that this is the most load-bearing issue: the entire style-alignment contribution depends on Sp being an adequate proxy for S1, and the paper offers only empirical support, no analytical treatment. The concrete diagnostic test would settle whether the surrogate can be gamed in a controlled setting. Since the reader already issued CONDITIONAL and my concern supports that verdict rather than overturning it, I recommend UNCHANGED.","tokens_in":34903,"tokens_out":7497,"duration_ms":87323,"concrete_test":"Construct a minimal diagnostic MDP with two states and two actions where the true style label z=1 iff a length-3 window has pattern (A,B,A), and the offline dataset contains only trajectories following A,B,A,A,B,A,... so p_{π_D}(z=1|s0,A)=1. Train SCIQL with the same window-labeling and χ-indicator reward. If the learned policy achieves high surrogate reward but low true S1 (e.g., by taking A repeatedly), the surrogate is not a valid proxy; if it still achieves high S1, the concern is resolved for this diagnostic.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central style-alignment claim rests on replacing the true non-Markovian objective S1 (Eq. 2) with the surrogate Sp (Eq. 5), using r(s,a)=p_{π_D}(z|s,a). The paper explicitly provides no bound: it only says 'We show experimentally that this objective still permits high alignment' (Sec. 3). This gap is not benign. p_{π_D}(z|s,a) is the conditional label probability under the behavior policy; a learned policy that visits the same (s,a) in a different temporal context can receive high surrogate reward while the actual window label is wrong. This is precisely the non-Markovianity the surrogate was introduced to avoid, and it is most severe for the w>1 labels actually used (e.g., turn direction, radius, curvature noise in Circle2d). The implementation makes the reward even more myopic: Eq. 12 sets χ to the per-transition indicator 1(z_c=z), not even the smoothed p(z|s,a). No consistency argument connects this reward to the evaluation metric S1 of Eq. 16. If the surrogate fails to transfer under distribution shift, the reported style-alignment margins may not reflect true alignment for novel policies.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a general definition of behavior style as a subtrajectory labeling function, casts style-aligned policy learning as a goal-conditioned offline RL problem, and introduces SCIQL, which combines IQL-style value learning with hindsight relabeling and a new Gated Advantage Weighted Regression (GAWR) rule. The conceptual objective (Eq. 7) is to maximize task return among policies that maximize style alignment. In practice, the true non-Markovian style objective is replaced by a Markovian surrogate reward r_D^λ(s,a)=p^λ_{π_D}(z|s,a), and the policy is extracted via weighted behavior cloning with a gated advantage ξ=Aλ+σ(Aλ)Ar. Experiments on Circle2d, HalfCheetah, and HumEnv across 7 datasets and 820 training runs report large improvements over CBC, BCPMI, SORL, and ablations in both style alignment and style-conditioned task performance.","tokens_in":35257,"tokens_out":6449,"duration_ms":80488,"significance":"If the empirical findings hold, this is a practically valuable contribution: the labeling-function formulation makes styles interpretable, cheap to specify, and directly measurable, while SCIQL demonstrates that value learning plus relabeling can improve style alignment and task performance from static heterogeneous data. The paper is strong on reproducibility: code, datasets, environment details, and per-criterion results are provided, and the evaluation is unusually extensive. However, the core method rests on two unsupported approximations — the Markovian surrogate style reward and the GAWR projection — and the paper's strongest claims are stated in absolute terms. The contribution is therefore primarily empirical; the theory gaps need to be addressed before the claims can be accepted at face value.","major_comments":[{"comment":"The central style-alignment claim rests on an unproven surrogate. Eq. (5) replaces the true objective S1 (Eq. 2) with Sp via r_D^λ(s,a)=p^λ_{π_D}(z|s,a), with no bound or consistency argument; Sec. 3 only promises experimental support. The actual TD target in Eq. (12) is more myopic: χ_ind=1(z=z_c) is a per-transition indicator, and with z sampled uniformly over labels (p_r, App. B), the target is 1 only when the sampled label matches the dataset label; this is not p(z|s,a) in any update, and ignores w>1 temporal dependence. Under distribution shift, a policy can visit the same (s,a) in a different context and receive reward for the wrong window label. Table 3 shows symptoms (Circle2d movement direction: 20.5–27.0 alignment, vs. near-100 for position). A formal bound or a direct Sp-vs-S1 transfer experiment is required to support 'best style alignment by a large margin.'","section":"Sec. 3, Eq. (5); Sec. 4.2, Eq. (12)"},{"comment":"GAWR is presented as approximating the projection onto the style-optimal set Π_λ(z) of Eq. (7), but no derivation, convergence result, or error bound is given. The gated combination ξ=Aλ+σ(Aλ)Ar is a heuristic whose sigmoid makes it scale-dependent; the EMA normalization mentioned after Eq. (14) is not specified in Appendix B. Table 4 shows the gate can hurt style: halfcheetah-stitch-v0 style drops from 78.0 (SCIQL λ) to 60.8 (SCIQL λ>r), and several Circle2d criteria collapse under r>λ. Since the task-performance claims depend on this rule, the paper should either characterize the implied constraint formally (e.g., a style-return lower bound under a margin condition) or compare systematically against a constrained-RL baseline and an unweighted sum ablation.","section":"Sec. 4.3, Eq. (14)"},{"comment":"The hypervolume claims are not fully supported. SORL is evaluated at only β=0,1,3 and SCIQL at only λ, λ>r, r>λ; these six points do not define a Pareto front, so the reported '+41.2% to +163.9%' hypervolume improvement is an artifact of the chosen configurations. The text also reports an '18–28%' reduction in Euclidean distance to the ideal point without giving the formula, the reference point, or confidence intervals. Please report the exact hypervolume definition, the set of points used, and seed-level variability, or compute a true Pareto hull over all evaluated trade-off settings.","section":"Sec. 5.3, Fig. 2"}],"minor_comments":[{"comment":"There is a missing reference placeholder: 'Also in the online RL setting, ?model the stylized policy learning problem...' should cite the relevant constrained-MDP/online stylized-RL papers.","section":"Sec. 2, Related Work"},{"comment":"The main-text claim 'best style alignment performance by a large margin compared to previous baselines for every dataset' is true for the averaged dataset scores, but Table 3 shows SCIQL is below CBC/BCPMI on Circle2d turn direction, radius, and speed. This is acknowledged in Appendix D, but the main text should qualify 'average across criteria' more visibly.","section":"Sec. 5.2 / Table 3 / App. D"},{"comment":"Algorithm 1 lists three sequential 'while not converged' loops, while the text says value learning and policy extraction 'can be performed jointly in practice.' Clarify which training scheme was actually used for the reported results.","section":"Algorithm 1 / Sec. 4.3"},{"comment":"The GAWR normalization ('advantages can be normalized with an exponential moving average') is not specified — EMA decay, normalization target, and whether it is applied to Aλ, Ar, or both should be given.","section":"Appendix B"},{"comment":"Table 4 caption and the accompanying text refer to 'Table??' instead of the actual table number. Several references are incomplete or duplicated in the bibliography; a final proofread is needed.","section":"Various"}],"recommendation":"major_revision","confidential_remarks":"The paper is a solid empirical contribution with unusually extensive evaluation and reproducible code. The main risk is the unsupported gap between the surrogate style reward actually optimized and the true alignment metric used for evaluation; this is load-bearing for the headline claims. I do not think rejection is warranted — the gap can be narrowed with additional analysis and experiments — but it must be addressed before publication. The GAWR and hypervolume claims are secondary but also require strengthening."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a solid empirical paper that deserves refereeing. The core contribution is SCIQL, which combines IQL with hindsight relabeling and a gated AWR update to optimize style alignment and task performance from a static dataset. The experiments are extensive — 7 datasets, 820 training runs, multiple ablations — and the gains are generally large and consistent across environments, especially on HalfCheetah and HumEnv.\n\nWhat is genuinely new: the subtrajectory-window labeling function formulation is a useful generalization of the trajectory-level labels in CTV AE and BCPMI, and the GAWR gating mechanism is a simple, effective way to add task reward signals without trashing the style. The paper also includes a careful evaluation protocol and releases code and data, which is more than many papers in this area do.\n\nThe soft spots are in the theory. The surrogate Markov reward in Eq. (5) replaces the non-Markovian true objective Eq. (2) without any bound; the paper explicitly says “we show experimentally” that it works. The stress-test concern is legitimate: the implementation actually uses a per-transition indicator (Eq. 12), which is even more myopic, and for window-based labels (turn direction, radius, curvature noise) a learned policy could in principle exploit the surrogate by visiting the same (s,a) in a different temporal context. That said, the evaluation is on the true metric S1 (Eq. 16), not on the surrogate, so the paper is not circular — it just lacks a theoretical safety net. A reviewer should push for at least a characterization of when the surrogate can diverge, or a consistency argument.\n\nThe other soft spots are less serious. The GAWR gate is a heuristic; it works empirically, but there is no analysis of what it is doing to the style-optimal set. The estimator choice in Appendix E.1 is made by looking at the final style-alignment numbers, and the global choice of style sampling distribution p_r is not optimal for all criteria (Appendix E.2 shows p_c would help on several Circle2d criteria). These are tunable, but they mean the headline numbers come with some selection-on-metric caveat. Finally, the manuscript has artifacts: a missing citation (“?model” in Related Work), a Table?? reference, and inconsistent project URLs. That suggests the paper was rushed out.\n\nBottom line: the central claim — that SCIQL beats prior methods on both axes — holds up well in the experiments. The theoretical gap is real but not fatal, because the evaluation targets the true objective. I would send this to peer review, with a request for a stronger theoretical treatment of the surrogate and a cleanup of the manuscript. The paper is worth citing for anyone working on style-conditioned offline RL.","headline":"Strong empirical paper on style-conditioned offline RL with a real but non-fatal theoretical gap in the surrogate style reward; deserves a serious referee.","tokens_in":35682,"tokens_out":3355,"would_cite":true,"duration_ms":37598,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Style-conditioned offline reinforcement learning can be built by treating styles as goals, using a gated advantage to add task reward without losing alignment.","keywords":["offline reinforcement learning","style alignment","goal-conditioned RL","implicit Q-learning","advantage-weighted regression","trajectory relabeling","labeling functions","stylized policies"],"falsifier":"Run SCIQL on a dataset where the label predictor p(z|s,a) is deliberately misaligned with the true window-based style labels (for example, state-action pairs labeled z that never appear near a window labeled z), then measure the empirical alignment S1 on held-out trajectories; if SCIQL achieves high surrogate reward but low true alignment, the transfer assumption is falsified.","tokens_in":34781,"feed_emoji":"🤖","tokens_out":2635,"duration_ms":32561,"temperature":0.7,"pith_summary":"This paper tries to establish a unified, practical way to learn offline policies that both follow a prescribed behavior style and achieve high task rewards. It defines a style as the label of a subtrajectory under a user-supplied labeling function, which makes styles interpretable, cheap to annotate, and measurable. On top of that definition, the paper proposes SCIQL, an offline algorithm that adapts implicit Q-learning with hindsight relabeling and a new Gated Advantage Weighted Regression mechanism to choose actions that improve task performance only when they are unlikely to hurt style alignment. The authors report that SCIQL achieves the best style alignment across every tested dataset and improves the style-versus-task Pareto hypervolume by 41% to 164% over the strongest baseline.","feed_headline":"SCIQL beats prior offline style RL by 41-163% on joint metric","feed_subtitle":"A gated advantage trick lets agents follow prescribed behavior styles from static data without sacrificing task reward.","key_machinery":"The central object is the definition of a behavior style as the label of a subtrajectory under a labeling function lambda, replacing episode-level labels with per-step window labels to handle multi-timescale styles and credit assignment. The load-bearing mechanism is the surrogate Markov style reward r^lambda_D(s,a) = p^lambda_{pi_D}(z|s,a), estimated from the offline dataset, which turns style alignment into a standard MDP objective. SCIQL combines implicit Q-learning for style-conditioned value functions with hindsight relabeling (sampling style labels outside the joint distribution) and a Gated Advantage Weighted Regression update, xi = A^lambda + sigma(A^lambda) * A^r, which approximates","core_discovery":"The central claim is that SCIQL simultaneously achieves superior style alignment and style-conditioned task performance compared to prior offline methods. Style alignment is optimized by replacing the true non-Markovian style objective with a dataset-estimated Markovian surrogate reward, r^lambda_D(s,a) = p^lambda_{pi_D}(z|s,a), then learning style-conditioned value functions through expectile regression with hindsight style relabeling. Task performance is added through Gated Advantage Weighted Regression, which weights behavior cloning by a gated combination of the style advantage and the task advantage, so task improvement is allowed mainly when the style signal is positive. The paper repo","pith_inferences":["If the surrogate Markov reward is a faithful proxy for the true window-based style objective, the same framing could be applied to unsupervised or learned style criteria, replacing labeling functions with learned trajectory encoders and retaining the same SCIQL machinery.","The gating idea, using a sigmoid on the style advantage to scale the task signal, is a generic mechanism that could be transferred to other multi-objective offline RL settings, such as safety or constraint satisfaction, wherever one objective must be preserved while a second is optimized.","The paper leaves open the question of multiple simultaneous style criteria; a natural testable extension is whether separate style value functions and gates can be composed, or whether a single criterion needs to be hand-selected.","Because the style reward is estimated from the dataset, the approach may inherit dataset bias: styles that are rare or absent in the data cannot be reliably aligned, so a practical test is whether SCIQL degrades gracefully as label coverage shrinks."],"forward_implications":["If the surrogate style reward transfers, offline agents can be trained to exhibit user-defined styles from static, heterogeneous datasets without manual trajectory labels or environment interaction.","Hindsight relabeling enables styles that never appear together in a single training trajectory, because the policy can stitch behaviors across different demonstrations.","The gated advantage mechanism allows task-performance improvement without sacrificing style alignment, effectively shifting the Pareto frontier of style-versus-task outcomes toward the ideal point.","SCIQL's alignment is robust to label noise: it maintains better alignment than all baselines under noisy annotations and only converts to following wrong labels past a clearly computable noise threshold.","The framework extends goal-conditioned RL ideas to stylized policies, potentially opening a route to zero-shot style-conditioned control at inference time."],"fun_headline_variants":["SCIQL: offline RL that aligns style without losing reward","Gated advantage trick unlocks style-aligned offline RL","Offline style RL: SCIQL beats prior by 41-163%","Style-conditioned offline RL? SCIQL does it all"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The paper assumes, without proof, that the dataset-estimated Markovian style reward p^lambda_{pi_D}(z|s,a) is an adequate proxy for the true non-Markovian style alignment objective; the authors state this is only supported experimentally, and if optimizing this surrogate does not transfer to the true metric, the central style-alignment claim fails.","fun_headline_variants_meta":{"raw":{"variants":["SCIQL: offline RL that aligns style without losing reward","Gated advantage trick unlocks style-aligned offline RL","Offline style RL: SCIQL beats prior by 41-163%","Style-conditioned offline RL? SCIQL does it all"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000418,"raw_usage":{"total_tokens":1960,"prompt_tokens":685,"completion_tokens":1275,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":429,"completion_tokens_details":{"reasoning_tokens":1205}},"tokens_in":429,"tokens_out":1275,"duration_ms":12628,"temperature":1.0,"reasoning_tokens":1205,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-03T06:22:40.550325+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run SCIQL on a dataset where the label predictor p(z|s,a) is deliberately misaligned with the true window-based style labels (for example, state-action pairs labeled z that never appear near a window labeled z), then measure the empirical alignment S1 on held-out trajectories; if SCIQL achieves high surrogate reward but low true alignment, the transfer assumption is falsified.","supporting_citations":[],"review_version":1}