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REVIEW 2 major objections 4 minor 12 references

Learning When to Trust via Selective Context Preference Optimization

T0 review · 2 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper argues that language models should be trained to judge external context on its merits, and that a balanced preference-optimization scheme roughly halves the rate at which misleading hints flip correct answers to wrong.

desk verdict MIST/SC2W and the SCOPE recipe are solid, reproducible contributions, but the 'selective trust' headline runs ahead of the evidence since correct context never supplies information the question lacks. read the letter →

arxiv 2608.06377 v1 pith:4GFBTH3P submitted 2026-08-06 cs.CL cs.AIcs.LG

Xian Sun , Wei Chow , Yingshuo Wang , Junhao Liu , Wei Gao , Qing Wu , Lingdong Kong This is my paper
classification cs.CLcs.AIcs.LG
keywords selectivetrustmisleadingcontextDirectPreferenceOptimizationSC2WMISTbenchmarkrobustnesslanguagemodelalignmentsycophancy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that resisting misleading context is the wrong training target, because a model that ignores all outside signals looks robust in tests that only compare clean prompts with prompts carrying wrong hints, yet is useless when the context is worth trusting. It recasts the problem as selective trust and introduces MIST, a human-annotated benchmark that renders each reasoning item under four matched conditions—clean, misleading, correct-context, and irrelevant-context—so that only the added signal changes. Across 23 models the authors report that a single misleading signal costs 17.1 points of accuracy on average. They then propose SCOPE, which mines cases where a base model answers correctly on its own but wrong under a misleading signal, and trains a standard DPO objective over matched preference pairs balanced equally across all four conditions. The paper's central claim is that this markedly lowers the signal-induced correct-to-wrong rate (SC2W) on two open-weight model families while preserving accuracy on clean, correct-context, and irrelevant-context inputs, and that the behavior transfers zero-shot to external benchmarks.

What carries the argument

The load-bearing object is the matched four-condition item design and the paired SC2W metric, together with the balanced preference construction. Each MIST item holds question, answer space, gold answer, and plausible wrong answer fixed while varying only the added context: none (clean), a signal toward a wrong answer (misleading), a signal toward the gold answer (correct), and non-answer-bearing text (irrelevant). SC2W is the fraction of items the model answers correctly under clean but wrongly under misleading, isolating signal-induced flips from raw ability. SCOPE reuses one chosen/rejected response pair across all four context prompts, samples the four conditions with equal 25% mass, and optimizes the standard full-completion DPO loss over the combined objective; the construction ablation is what the paper uses to argue that matched pairing and control balance are what matter rather than the loss function.

What would settle it

Construct a matched item set in which the clean condition is unanswerable without the correct-context passage, and compare a SCOPE-trained model against the base model and a context-ignoring baseline on those items; if the SCOPE model shows no accuracy gain over ignoring context, the claim that it learns selective trust is falsified.

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Extended reading notes

Core claim

The central discovery is that susceptibility to misleading context is universal and separable from task ability: SC2W conditions on items the model already solves when clean, and across 23 frontier and open-weight models a misleading signal flips these clean-correct answers to wrong 17.1 points on average. The paper further claims that resistance training is not selective trust: prompt-defense, supervised fine-tuning, and misleading-only DPO all raise misleading-context accuracy while eroding clean or correct-context accuracy. SCOPE keeps the standard full-completion DPO loss unchanged and changes only what enters it—matched signal-counterfactual pairs balanced equally across misleading, clean, correct-context, and irrelevant-context conditions. On Qwen3-4B it cuts SC2W from 35.0 to 16.3, and on Llama-3.2-3B from 31.5 to 20.6, while preserving or improving the three control accuracies; the learned behavior is claimed to transfer zero-shot to GSM-IC, GSM-Plus, and Sharma-style sycophancy evaluation.

Load-bearing premise

The load-bearing premise is that the benchmark's correct-context condition proves the model benefits from correct context; because every clean question is answerable on its own, a model that ignores all added context would score well on every reported condition, so the numbers alone do not distinguish selective trust from balanced context-invariance.

Editorial extensions

If this is right

  • Context-robustness evaluation should use matched multi-condition designs, since single-condition tests cannot distinguish a model that distrusts all context from one that judges signals correctly.
  • Training with balanced control pairs should roughly halve SC2W on open-weight families: the paper reports reductions from 35.0 to 16.3 on Qwen3-4B and from 31.5 to 20.6 on Llama-3.2-3B.
  • Resistance-only baselines are expected to trade correct-context accuracy for misleading-context accuracy; the paper reports Standard-DPO collapsing correct-context accuracy on Llama-3.2-3B.
  • The learned behavior is claimed to transfer zero-shot to GSM-IC, GSM-Plus, and sycophancy-style items, meaning the balanced preference data teaches a general stance rather than a prompt template.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: the reported metrics are also consistent with a model that simply ignores all added context, because MIST's clean questions are answerable on their own and the correct-context condition never requires using the context to improve on clean reasoning.
  • Inference: the external transfer tasks (GSM-IC, GSM-Plus, sycophancy-style items) likewise never reward using a helpful context, so zero-shot transfer does not by itself demonstrate benefit-from-correct-context behavior.
  • Inference: a direct test would add a condition in which the clean prompt cannot be answered without the correct context; if SCOPE-trained models improve on such items relative to base, selective trust is real, and if not, the method has produced balanced context-invariance.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper introduces MIST, a human-annotated benchmark of 1,000 reasoning items each rendered under four matched conditions (clean, misleading, correct-context, irrelevant-context), together with SC2W, a paired metric that measures how often a misleading signal flips a clean-correct answer to wrong. Using MIST, the authors document substantial susceptibility to misleading signals across 23 API and open-weight models. They then propose SCOPE, which constructs matched preference pairs from a base model's clean-correct/misleading-wrong failures and applies a standard DPO objective with equal sampling across the four conditions. Experiments on two trainable families (Qwen3-4B and Llama-3.2-3B) show large SC2W reductions while preserving or improving the three control accuracies, with construction ablations and zero-shot transfer to GSM-IC, GSM-Plus, and Sharma-style sycophancy tasks.

Significance. The empirical work is careful and well executed: training and evaluation pools are item-disjoint, answer scoring is deterministic exact match, the matched-condition design controls for task difficulty, and the ablations isolate the contribution of matched pairing and the control components. The benchmark and SC2W metric are potentially useful community resources, and the reported robustness gains on two model families are credible. However, the central conceptual claim that SCOPE learns 'selective trust'—defined in the introduction as including the ability to 'benefit from context that is actually correct'—is broader than what the evaluation can support. Because the clean condition is answerable on its own, the correct-context rows add only answer-redundant hints, and none of the external transfer tasks rewards using a beneficial context. The evidence supports a claim of balanced robustness to misleading and irrelevant signals with preserved clean reasoning, but not the stronger claim that the model learns to trust context that supplies genuinely new information.

major comments (2)
  1. [§4, Eq. (5); §3.1, Appl. A.4] The central claim that SCOPE learns 'selective trust,' defined in the introduction as including 'benefit from context that is actually correct,' is not supported by the reported evaluation. In Appl. A.4 the clean condition is required to be 'answerable on its own,' so the correct-context rows in MIST add only answer-redundant hints; the benchmark never presents a case where the correct context supplies information not already available in the question. Moreover, in Eq. (5) the chosen response r+ for the correct-context pair is the same context-free clean-condition response used under the other conditions, so the DPO objective for D_cor rewards producing the clean answer under a correct-context prompt rather than rewarding use of the context. Consequently the observed correct>clean gaps (e.g., Qwen3-4B: 98.1 vs 95.0; Llama-3.2-3B: 80.0 vs 72.0 in Table 1) rule out pure context-invariance but demonstrate only that the model benefits from redundant hints; they cannot distinguish 'selective trust' from a heuristic that trusts context only when it confirms the model's own tentative answer. Please either add a condition or a separate task in which the correct answer is only derivable from the provided context and show that SCOPE improves there, or reframe the paper's claim to 'balanced robustness to misleading signals with preserved accuracy on clean, correct, and irrelevant contexts.'
  2. [§5.4, Table 2] The external-transfer claim that 'the learned behavior transfers zero-shot' is presented as evidence that SCOPE 'transfers by learning when to rely on context, not by ignoring it.' However, none of the three external suites rewards using a beneficial context: GSM-IC adds irrelevant distractors, GSM-Plus perturbs problem statements, and Sharma tests sycophancy. These results therefore support the transfer of robustness to misleading and irrelevant signals, but not the transfer of selective use of helpful context. The phrasing in §5.4 goes beyond what the metrics can distinguish; please temper the claim or add an external task with a helpful-context condition.
minor comments (4)
  1. [Fig. 7(a)] The panel reports 188 rescued (56.8%) out of 331 vulnerable items and also a net of +182 repairs with 200 gains and 18 costs; the relationship between these counts (for example, why repairs are fewer than gains) is not explained in the text.
  2. [§5.6, Table 5] The SC2W human-audit Spearman correlation is 0.82 with a 95% bootstrap CI of [0.30, 1.00] based on only six systems, so the claim that 'agreement is strong across all six metrics' is too strong for SC2W; please temper the wording or report the interval alongside the strength claim.
  3. [Conclusion] The statement that SCOPE 'roughly halves SC2W' is accurate for Qwen3-4B (35.0 to 16.3) but not for Llama-3.2-3B (31.5 to 20.6); consider replacing 'roughly halves' with 'substantially reduces.'
  4. [Conclusion] The claim 'a single misleading signal costs 17.1 points on average' should define the underlying quantity at the point of use, since the body defines only SC2W and the conclusion appears to refer to the clean-minus-misleading accuracy gap rather than SC2W itself.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SCOPE trains on gold-scored, item-disjoint preference pairs; the external-transfer results are zero-shot; author-overlapping citations are contextual, not load-bearing.

full rationale

The paper's derivation chain is MIST measurement to SCOPE preference construction to held-out MIST evaluation and zero-shot external transfer. No link reduces to its input by construction. SCOPE's chosen responses are base-model completions verified against human gold answers (Sec. 4: "use that base model's clean-condition correct response when available"; App. I.3), and rejected responses are the base's own wrong misleading-condition completions; the DPO objective is standard with fixed weights ("the weights are fixed before evaluation and are not tuned on MIST test set"). The training pool is item-disjoint from the evaluation pool ("ensure that the training pool shares no items with MIST"; "Training and evaluation share no items"), so the reported SC2W drop is not a fit to the scored set. External transfer uses no target-benchmark examples for training or selection (App. C.1: "no example from these benchmarks is used for training, early stopping, hyperparameter selection, or adapter selection"), making those results genuine predictions. The author-overlapping citations (Chow et al. 2024; Xu et al. 2025; Liang et al. 2026; Zhou et al. 2026) appear alongside canonical references in related-work or implementation context and are not invoked as a uniqueness theorem or as the method's justification. The paper's own limitation passage correctly notes that matched-prompt robustness is not evidence of chain-of-thought faithfulness. The one material weakness is construct validity, not circularity: App. A.4 requires the clean condition to be "answerable on its own," so the correct-context condition adds only answer-redundant hints, meaning "benefit from context that is actually correct" is not separately demonstrated beyond context preservation; that is an evaluation-scope caveat, not a derivation that is equivalent to its input.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central empirical claims rest on standard deep learning assumptions and on the validity of the benchmark's matched-conditions design. No new theoretical entities are introduced. The principal free parameters are the objective balancing weights, which are fixed as equal sampling in the headline runs.

free parameters (3)
  • Objective balancing weights (lambda_m, lambda_c, lambda_p, rho) = (0.25, 0.25, 0.50, 0.50)
    Weights for the four DPO components in Eq. 10; set by hand to achieve equal per-condition sampling. The paper states they are fixed before evaluation and not tuned on MIST test set, but they are a design choice that affects the reported balance.
  • beta (DPO temperature) = 0.1
    Standard DPO hyperparameter, fixed across all comparisons (Appl. I.4).
  • Training budget = 300 steps
    Fixed step count for all trainable methods; a 60-step checkpoint is reported for mechanism analysis (Appl. I.4).
assumptions (4)
  • domain assumption The four matched conditions (clean, misleading, correct-context, irrelevant-context) differ only in the added context; question, answer space, and gold answer are fixed.
    This is the foundation of MIST's validity (Sec. 3.1, Appl. A.4). If conditions differ in difficulty or leakage, SC2W would confound signal effects with item effects.
  • domain assumption Clean items are answerable on their own.
    Appl. A.4: 'Clean: the original task with no added context. It must be answerable on its own.' This makes the correct-context condition non-necessary, enabling the context-invariance loophole.
  • domain assumption The deterministic exact-match scorer is a valid measure of answer correctness.
    Appl. B.1: rule-based parsing of final answers. This is standard but assumes parsing ambiguity is negligible.
  • standard math DPO on full-completion responses with beta=0.1 reliably optimizes the preference objective.
    Uses the standard DPO loss (Rafailov et al., 2023) with standard hyperparameters; accepted as a tool in the field.

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Cite this review

Pith. "Pith review of Learning When to Trust via Selective Context Preference Optimization." pith.science (2026). https://pith.science/paper/4GFBTH3P

@misc{pith2026260806377,
  author       = {Pith},
  title        = {Pith review of: Learning When to Trust via Selective Context Preference Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4GFBTH3P}},
  note         = {Machine review of arXiv:2608.06377}
}
read the original abstract

Language models increasingly condition their answers on external signals, and a single misleading one can turn a correct answer wrong. The obvious remedy, training models to resist such signals, hides a failure mode: a model that ignores all context looks robust yet is useless when the context is worth trusting. We recast the problem as selective trust and introduce MIST, a human-annotated benchmark that renders each reasoning item under four matched conditions (clean, misleading, correct-context, and irrelevant-context), together with SC2W, a paired metric counting how often a misleading signal flips a clean-correct answer to wrong. Across a comprehensive benchmark study, we observe that such a susceptibility is universal. We then propose SCOPE, which mines clean-correct/misleading-wrong failures and optimizes a standard Direct Preference Optimization (DPO) objective over matched preference pairs balanced equally across all four conditions, rather than over misleading items alone. Our approach substantially reduces SC2W on popular open-sourced models while preserving accuracy when the added context is clean, correct, or irrelevant. With this work, we argue that models should be judged on selective trust, not on resistance alone.

Figures

Figures reproduced from arXiv: 2608.06377 by the authors.

Figure 1
Figure 1. Misleading signals cause a universal ac￾curacy drop. On MIST (Misleading Signal Testbed), replacing a clean context with a plausible misleading one lowers accuracy for every model we evaluate, fron￾tier proprietary models included. answer the moment the prompt carries a plausible but wrong suggestion. Such cues steer predictions without being faithfully reflected in the model’s stated reasoning (Turpin et al., 2023;… view at source ↗
Figure 2
Figure 2. The MIST construction pipeline. We combine items adapted from existing benchmarks with a realistic human-authored subset. Candidates are first screened for answer validity, difficulty, and realism. A team of STEM-trained annotators then creates clean, misleading, correct-context, and irrelevant-context variants, followed by independent verification and provenance logging before the benchmark is frozen. We also repor… view at source ↗
Figure 3
Figure 3. SCOPE framework. Matched preference construction balances misleading, clean, correct-context, and irrelevant-context pairs before DPO training. labels. For each retained item, r − is the same base model’s naturally terminated, non-truncated wrong response under the misleading condition. The cho￾sen response r + is selected by a base-specific rule: use that base model’s clean-condition correct re￾sponse when availabl… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: MIST-1000 coverage. The benchmark contains 1,000 items and 4,000 matched condition rows spanning diverse sources, topics, signal channels, and wrong-answer types. so the central robustness claim rests only on deter￾ministic answer correctness. Training and evalu￾ation …
Figure 5
Figure 5. Figure 5: SCOPE generalizes: best-balanced on MIST and best on unseen benchmarks. (Left) Our method is the one that beats Base on both robustness (SC2W) and control accuracy (shaded region), evaluated on Qwen3-4B ( [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Human judgments validate automatic scoring. Reference-assisted semantic-correctness win ratios align with automatic win ratios across all six main metrics. See Appl. Sec. G and Appl. Sec. J for additional details. the reference is shown and what the design can and cann…
Figure 7
Figure 7. Figure 7: Mechanism and training diagnostics. (a) SCOPE repairs 188 of the 331 Qwen3-4B failures triggered by misleading context, with 182 net repairs overall. (b) It improves misleading-context accuracy while preserving performance on clean, correct-context, and irrelevant-cont…
Figure 8
Figure 8. Figure 8: MIST slice diagnostics. Held-out MIST evaluation SC2W by (a) provenance, (b) answer format, and (c) misleading-signal source. Across all three groupings, SCOPE generally produces lighter cells than its base model, showing that the reduction is not confined to one slice…
Figure 9
Figure 9. Figure 9: Qualitative examples of repaired misleading-signal failures. Both examples are human-authored benchmark items with matched misleading, correct, and irrelevant context variants. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 10
Figure 10. Figure 10: Additional qualitative examples. Case 3 shows a repaired boolean conflict; Case 4 shows a remaining failure mode where the model handles controls but still follows a plausible misleading search snippet. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]
Figure 11
Figure 11. Figure 11: Longer human-authored qualitative examples. These cases show the common failure pattern where a model performs the needed reasoning but changes the final answer to match an answer-like external signal. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: Longer existing-benchmark qualitative examples. These cases demonstrate that the same selective￾trust behavior appears beyond hand-authored items, including domain-knowledge and multi-step arithmetic sources. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]
Figure 13
Figure 13. Figure 13: Additional longer qualitative examples. Case 9 shows rejection of a plausible science misconception encoded as an answer key; Case 10 shows policy-threshold reasoning where the model must ignore both a misleading label and a non-binding user preference. 25 [PITH_FULL…

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Works this paper leans on

12 extracted references · 2 canonical work pages

  1. [2]

    Anthropic

    SmolLM2: When smol goes big – data-centric training of a small language model.arXiv preprint arXiv:2502.02737. Anthropic. 2026. Claude Opus 4.8 model documenta- tion. Anthropic model documentation. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. Self-RAG: Learning to retrieve, generate, and critique through self-reflection. ...

    arXiv 2026

  2. [5]

    Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant

    Gemma 3 technical report.arXiv preprint arXiv:2503.19786. Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021. Did Aristotle use a laptop? a question answering benchmark with implicit reasoning strategies.Trans. Assoc. Comput. Linguist., 9:346–361. Google. 2026. Gemini 3.1 Pro preview model documen- tation. Google AI fo...

    arXiv 2021

  3. [6]

    Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. InProc. ACM Workshop Artif. In- tell. Secur . Bingxiang He, Zekai Qu, Zeyuan Liu, Yinghao Chen, Yuxin Zuo, Cheng Qian, Kaiyan Zhang, Weize Chen, Chaojun Xiao, Ganqu Cui, Ning Ding, and Zhiyuan Liu. 2025. JustRL: Scaling a 1.5B LLM with a sim-...

    arXiv 2025

  4. [7]

    the moon is made of marshmallows

    Entity-based knowledge conflicts in question answering. InProc. Conf. Empir . Methods Nat. Lang. Process. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When not to trust language models: Investigating effectiveness of parametric and non-parametric mem- ories. InAnnu. Meet. Assoc. Comput. Linguist. Yu ...

    arXiv 2023

  5. [8]

    North Am

    Are NLP models really able to solve simple math word problems? InConf. North Am. Chapter Assoc. Comput. Linguist., pages 2080–2094. 28 Ethan Perez, Sam Ringer, Kamil ˙e Lukoši ¯ut˙e, Karina Nguyen, Edwin Chen, et al. 2023. Discovering lan- guage model behaviors with model-written evalua- tions. InFind. Annu. Meet. Assoc. Comput. Linguist. Fábio Perez and ...

    arXiv 2023

  6. [9]

    Direct preference optimization: Your language model is secretly a reward model. InAdv. Neural Inf. Process. Syst. Mrinank Sharma, Meg Tong, Tomasz Korbak, David Du- venaud, Amanda Askell, Samuel R. Bowman, et al

    work page

  7. [11]

    Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover

    Sycophancy under pressure: Evaluating and mitigating sycophantic bias via adversarial dialogues in scientific QA.arXiv preprint arXiv:2508.13743. Siyan Zhao, Zhihui Xie, Mengchen Liu, Jing Huang, Guan Pang, Feiyu Chen, and Aditya Grover

    arXiv

  8. [2021]

    DeepSeek-AI

    Training verifiers to solve math word prob- lems.arXiv preprint arXiv:2110.14168. DeepSeek-AI. 2025. DeepSeek-R1: Incentivizing rea- soning capability in LLMs via reinforcement learning. Nature, 645:633–638. Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. 2024. KTO: Model alignment as prospect theoretic optimization. InInt....

    arXiv 2025

Show all 12 references
  1. [2023]

    Enabling large language models to generate text with citations. InProc. Conf. Empir . Methods Nat. Lang. Process., pages 6465–6488. Gemma Team, Aishwarya Kamath, Johan Ferret, et al

  2. [2024]

    Towards understanding sycophancy in lan- guage models. InInt. Conf. Learn. Represent. Zhanming Shen, Jintao Tong, Shaotian Yan, Chen Shen, Hao Chen, Wentao Ye, Xiaomeng Hu, Rui Miao, Haobo Wang, Junbo Zhao, Gang Chen, and Jieping Ye. 2026. Purified OPSD: On-policy self- distil...

    2026 arXiv

  3. [2025]

    Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al

    Phi-4-reasoning technical report.arXiv preprint arXiv:2504.21318. Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al

    arXiv

  4. [2026]

    Wei Zhou, Xiongwei Zhu, Zelin Xu, Bo Dong, Lixue Gong, Yongyuan Liang, Meng Chu, Leigang Qu, Lingdong Kong, Wei Liu, and Tat-Seng Chua

    Self-distilled reasoner: On-policy self- distillation for large language models.arXiv preprint arXiv:2601.18734. Wei Zhou, Xiongwei Zhu, Zelin Xu, Bo Dong, Lixue Gong, Yongyuan Liang, Meng Chu, Leigang Qu, Lingdong Kong, Wei Liu, and Tat-Seng Chua. 2026. DanceOPD: On-policy ge...

    2026 arXiv

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