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REVIEW 3 major objections 6 minor 38 references

CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation

T0 review · 3 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Text-to-video generation improves when the same prompt enhancer both writes the captions used to fine-tune the generator and rewrites user prompts at inference, even when a shared caption schema is already in place.

desk verdict Useful two-step recipe for closing the PE-caption gap, honestly reported — but the two-sided-alignment mechanism is stronger than the missing 2x2 cells can support. read the letter →

arxiv 2608.03046 v1 pith:TDIPEKTJ submitted 2026-08-04 cs.CV

Yizhuo Jia , Jingyun Hua , Yuanxing Zhang This is my paper · ORCID
classification cs.CV
keywords text-to-videogenerationpromptenhancementconditioningalignmentdiffusiontransformerrecaptioningcaptionschemaPE-Captiongapmaximummeandiscrepancy
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

Text-to-video diffusion transformers (DiTs) generate video from text and are trained on detailed captions, but at deployment they are conditioned on user prompts rewritten by a prompt enhancer (PE). This paper argues that even when training captions and inference prompts follow the same caption template, they still differ in detail selection, organization, granularity, and phrasing—a residual mismatch it calls the PE–Caption gap. CAPE-T2V attacks the gap on both sides: it first fine-tunes a PE to map concise captions, detailed captions, and pseudo user prompts into captioner-generated six-part targets, then uses that same Anchored PE to rewrite the dense captions used for DiT fine-tuning. At inference the same Anchored PE rewrites user prompts, so the text that trains the model and the text that conditions it come from one operator. In a matched comparison against a schema-aligned control, CAPE-T2V scores higher on StoryEval, VBench-2.0, and T2V-CompBench for both Wan2.2 and LTX-2.3, and its training captions sit closer to inference-time outputs as measured by embedding-based squared maximum mean discrepancy.

What carries the argument

The load-bearing object is the Anchored PE: a prompt enhancer fine-tuned so that three kinds of inputs—concise captions, detailed captions, and pseudo user prompts—all map to captioner-generated targets in a fixed six-part schema (Short Caption, Dense Caption, Main Object, Background, Camera, Style). This same frozen PE is then reused in both directions: it rewrites user prompts at inference and rewrites the video-derived dense captions used to fine-tune the DiT. The argument is carried by the inequality $D(P^{\mathrm{user}}_{\mathrm{PE}}, P^{\mathrm{dense}}_{\mathrm{PE}}) < D(P^{\mathrm{user}}_{\mathrm{PE}}, P^{\mathrm{dense}}_{\mathrm{rewriter}})$, where $D$ is instantiated as squared maximum mean discrepancy ($\mathrm{MMD}^2$) in a frozen text-embedding space. The schema alone fixes which parts appear and their order; the Anchored PE is what fixes their realization, while the DiT architecture and training objective remain unchanged.

What would settle it

A decisive test would compare a third DiT fine-tuned on captions from a same-size, same-data rewriter that is not the deployed PE: if that third DiT matches CAPE-T2V's scores and MMD2, the mechanism is not the two-sided use of the deployed PE.

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Extended reading notes

Core claim

The paper's central claim is that the identity of the caption writer matters beyond the schema it follows. Using the deployed PE to build DiT fine-tuning captions reduces the measured PE–Caption gap relative to a separately prompted schema-aligned rewriter: embedding-based MMD2 is 0.0682 versus 0.0764 in the token-length-matched comparison, and 0.0673 versus 0.0758 on the full set. CAPE-T2V beats the matched Schema-Aligned DiT on all six model–benchmark pairs, with gains of 1.6, 1.12, and 0.30 percentage points on StoryEval, VBench-2.0, and T2V-CompBench for Wan2.2, and 1.4, 0.92, and 0.65 for LTX-2.3. The anchoring step alone already improves both original generators, and the DiT adaptation adds further gains, so the two steps contribute separately. The paper concludes that two-sided conditioning alignment—not merely sharing a caption schema—is what narrows the residual mismatch.

Load-bearing premise

The matched control assumes the two fine-tuning paths differ only in which caption operator writes the captions, but the operators also differ in model size, fine-tuning, and prompt design, and the benchmark differences are reported without significance tests, so the gains cannot be uniquely pinned on two-sided alignment.

Editorial extensions

If this is right

  • Deployed text-to-video systems can narrow the conditioning gap by recaptioning their post-training data with the same prompt enhancer they use at inference, without touching the generator architecture or objective.
  • A shared caption schema is not enough; the caption operator that produces fine-tuning captions must match the operator that rewrites user prompts.
  • The recipe works without access to a generator's native captioning pipeline, making it applicable across models whose internal captioning recipes are unknown.
  • Both steps pay off separately, so systems that already use a strong prompt enhancer can still gain from adapting the generator to that enhancer's output distribution.
  • The measured reduction in embedding-space discrepancy indicates the residual mismatch is quantifiable and can be tracked without video generation, as a cheaper diagnostic.

Reading between the lines

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

  • A sharper test of the paper's mechanism would equalize model size and training data between the two caption operators; because the prompted rewriter is a much larger model than the fine-tuned Anchored PE, part of the reported gain could come from operator strength rather than from two-sided alignment.
  • The pseudo-user-prompt and detailed-source input types are not ablated individually, so an extension is to measure how much each construction contributes to closing the gap; the paper itself flags that this remains open.
  • If the mechanism generalizes, any conditional generative model with an inference-time text rewriter—such as image or audio generators—should benefit from the same two-sided recipe, a prediction this paper does not test.
  • The choice of caption schema is a free parameter; varying which parts it includes could reveal what makes a schema a good anchor and whether simpler schemas leave more residual gap.
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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

3 major / 6 minor

Summary. The paper introduces CAPE-T2V, a two-step method for aligning the conditioning text distributions in text-to-video DiTs. In Step 1, a Qwen3.5-9B prompt enhancer is fine-tuned to map concise captions, detailed captions, and pseudo user prompts to captioner-generated six-part MiraData-style targets, yielding an 'Anchored PE.' In Step 2, the same per-video dense captions are rewritten either by the Anchored PE (for the PE-Aligned DiT) or by a prompted Qwen3.5-397B-A17B rewriter (for the Schema-Aligned DiT), and each DiT is fine-tuned on its caption set. At inference, both DiTs are conditioned on Anchored-PE rewrites of user prompts. The paper reports that CAPE-T2V outperforms the Schema-Aligned control on StoryEval, VBench-2.0, and T2V-CompBench for both Wan2.2 and LTX-2.3, with gains of 1.6, 1.12, and 0.30 percentage points (Wan2.2) and 1.4, 0.92, and 0.65 points (LTX-2.3). It also reports lower MMD2 between Anchored-PE fine-tuning captions and Anchored-PE inference rewrites than between prompted-rewriter captions and those same rewrites, and it shows that Anchored-PE captions do not simply copy the dense sources.

Significance. The proposed recipe is practically relevant: it requires no access to a deployed generator's native captioning recipe, and it is evaluated under a carefully matched protocol in which videos, dense-caption sources, schema, optimization settings, training budget, and inference-time PE are held fixed. The use of three external benchmarks across two different model families, the inclusion of paired bootstrap/permutation intervals for the MMD2 diagnostics in Appendix E, and the unusually candid Section 5 limitations paragraph are all strengths. If the central claim is accepted, the work provides a simple and transferable way to reduce training–inference conditioning mismatch for T2V models. However, the current experimental design does not uniquely support the 'two-sided alignment' mechanism, and the benchmark gains lack statistical characterization.

major comments (3)
  1. [Section 3.2 and Table 1] The matched comparison is not matched on operator identity. The Anchored PE is Qwen3.5-9B after SFT on about 735K pairs, while the prompted rewriter is Qwen3.5-397B-A17B with a fixed six-part instruction; these differ in scale, base training, fine-tuning status, and prompting. The paper's statement that 'the two paths differ only in which caption operator produces the DiT fine-tuning captions' (Section 3.2) is therefore true only in a narrow sense that bundles several independent attributes into the word 'operator.' The observed gains on StoryEval, VBench-2.0, and T2V-CompBench could equally arise from the Anchored PE producing captions that are better conditioning text on their own, independent of any matching with the inference-time PE. Moreover, the 2x2 factorial design that would separate these explanations—prompted rewriter as inference PE with both caption sets, and Anchored PE as inference PE with both caption sets—is never run; only the two Anchored-PE-inference cells are evaluated. I recommend adding the missing cells or explicitly reframing the contribution as 'captions written by the deployed prompt enhancer improve DiT fine-tuning,' with a discussion of why the residual operator confound does not affect that claim.
  2. [Section 4.3 and Appendix E.1] The MMD2 diagnostic cannot distinguish the paper's alignment mechanism from a same-model proximity effect. The reference distribution P^user_PE consists of Anchored-PE rewrites, so the comparison P^user_PE versus P^dense_PE involves two sets of texts produced by the same fine-tuned model, whereas P^user_PE versus P^dense_rewriter involves texts from different models. Lower MMD2 for the first pair is expected from shared model-specific phrasing and generation habits even if the Anchored-PE captions have no video-grounded advantage. Appendix E.1 correctly cautions that the analysis does not imply that the underlying text distributions are identical or explain the video-score gains, but Section 5 draws the stronger inference that the smaller discrepancy 'reflects the caption operator's realization rather than increased copying.' To make the diagnostic informative, the authors should add the contrast against rewrites produced by the prompted rewriter (the inference-time text under the alternative mechanism), and ideally connect caption-set identity to per-prompt benchmark scores through a randomization or regression analysis.
  3. [Table 1 and Section 5] The headline benchmark gains are single-run observations without confidence intervals or significance tests. Each DiT configuration is fine-tuned once, as the paper acknowledges in Section 5, and the evaluation protocol averages nine videos per prompt. The smallest reported gains (0.30 percentage points on Wan2.2 T2V-CompBench and 0.92 points on Wan2.2 VBench-2.0) are small relative to the likely variance from PE sampling seeds, latent-noise seeds, and judge stochasticity; the StoryEval judge is an LLM and the VBench-2.0 Diversity protocol is separate. The claim of 'a consistent advantage' across all six model–benchmark pairs would be much stronger with a bootstrap over prompts for the aggregate scores or with at least one additional fine-tuning seed. Without such analysis, the central quantitative result is a directional pattern rather than an established effect.
minor comments (6)
  1. [Table 1] The column headings for the StoryEval and VBench-2.0 sub-scores are difficult to parse because the main-text table appears to merge several heading levels; please reformat the table so each numeric column is clearly labeled and verify that the StoryEval Overall column reproduces the gains quoted in Section 4.2.
  2. [Table 3 and Appendix Table 9] The metric labeled simply 'BLEU-4' in Table 3 should specify that it is corpus-level BLEU-4 with the SacreBLEU signature, consistent with Appendix Table 9, so that readers do not confuse it with the mean sentence-level BLEU reported in the appendix.
  3. [Figure 3] The caption uses markers such as 'O1', 'D1', 'G1', and 'P1' without defining them; please add a sentence explaining the annotation scheme or remove the markers.
  4. [Section 2] The notation 'captions→PE' and 'PE→captions' for the coupling direction is introduced without definition; please state explicitly that the arrow indicates which distribution defines the target form for the other.
  5. [Section 5] The sentence 'we report no numbers for published prompt enhancers' is slightly overstated, since the released Official PE baselines are themselves published configurations; consider rephrasing to 'third-party prompt enhancers beyond the released system PEs.'
  6. [Section 4.1 and Appendix B] Please note in the main text that the StoryEval judge is GPT-5.5 rather than the judge used in the official StoryEval protocol, and state how this substitution affects comparability with published StoryEval scores; the appendix already says the judge backend is runtime configuration, but the main text should be equally explicit.

Circularity Check

1 steps flagged · score 3.0 of 10

MMD diagnostic is self-referential, but the central benchmark claim is external and not circular.

  1. self definitional [Section 3 (Problem setup) and Section 4.3 / Table 2]
    "P user PE denotes Anchored PE outputs from user prompts and represents the inference-time conditions. Given video-derived dense captions, P dense PE and P dense rewriter denote outputs from the Anchored PE and the prompted rewriter, respectively. We hypothesize ... D(P user PE, P dense PE) < D(P user PE, P dense rewriter)."

    The reference distribution P user PE and the CAPE-T2V training-caption distribution P dense PE are both produced by the same Anchored PE. The MMD2 comparison is therefore a self-similarity measure between two output sets of one model rather than an independent test of two-sided alignment; a smaller within-model MMD is expected by construction because the same caption operator writes both sides. The paper itself limits the claim in Appendix E.1 ('they do not imply that the underlying text distributions are identical or explain the video-score gains'), so this circularity is confined to the diagnostic and does not infect the external benchmark comparisons.

full rationale

The central claim—CAPE-T2V outperforms a Schema-Aligned DiT on StoryEval, VBench-2.0, and T2V-CompBench across Wan2.2 and LTX-2.3—is supported by external benchmarks whose scoring is independent of the method's internal definitions; no fitted parameter is renamed as a prediction, and no load-bearing self-citation or imported uniqueness theorem appears. The only self-referential element is the MMD2 gap analysis, which compares the Anchored PE's own fine-tuning captions with the Anchored PE's own inference rewrites, making a smaller MMD partly a same-model self-similarity artifact. Because the paper explicitly disclaims that the MMD explains the video-score gains and the headline results rest on standard benchmarks, the derivation chain is largely self-contained. The matched-control comparison is also imperfect—the Anchored PE (Qwen3.5-9B SFT) and prompted rewriter (Qwen3.5-397B-A17B) differ in scale and fine-tuning—but that is a controllability limitation, not circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the reliability of Qwen3.5-397B-A17B for captioner targets, dense captions, and consistency judgments; on the choice of the six-part MiraData schema as the anchor; and on the assumption that fine-tuning the DiT on Anchored PE captions does not degrade other capabilities. The schema is explicitly acknowledged in the Discussion as a free parameter. The MMD-based gap measure is an analytic choice, not a fitted parameter.

free parameters (2)
  • Caption schema (six-part MiraData schema) = Short, Dense, Main Object, Background, Camera, Style
    The schema is a hand-chosen design decision; the paper's Discussion calls the anchor a free parameter whose content may determine how much of the gap can be closed (Section 5).
  • Prompted rewriter instruction = Fixed six-part instruction to Qwen3.5-397B-A17B
    The control constructor is implemented with a specific instruction; the instruction content affects the control captions and hence the matched comparison.
assumptions (3)
  • domain assumption Qwen3.5-397B-A17B captioner-generated targets and consistency judgments are reliable enough to supervise PE training and DiT fine-tuning.
    The entire Step 1 and Step 2 data construction relies on outputs of Qwen3.5-397B-A17B for captioner targets, dense captions, and consistency checks (Sections 3.1, 4.1, Appendix A).
  • domain assumption MMD^2 in the Qwen3-VL-Embedding-8B space is a meaningful measure of the PE-Caption gap.
    The paper instantiates the discrepancy D as MMD^2 in a fixed embedding space; the choice of embedding and kernel affects the measured gap (Section 4.3, Appendix E.1).
  • domain assumption Fine-tuning the DiT on Anchored PE captions preserves or improves the DiT's other capabilities.
    The paper does not measure catastrophic forgetting or capability regression; it assumes the fine-tuning on 54K pairs is beneficial (Section 4.2).

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Cite this review

Pith. "Pith review of CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation." pith.science (2026). https://pith.science/paper/TDIPEKTJ

@misc{pith2026260803046,
  author       = {Pith},
  title        = {Pith review of: CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TDIPEKTJ}},
  note         = {Machine review of arXiv:2608.03046}
}
read the original abstract

Text-to-video (T2V) diffusion transformers (DiTs) are trained with detailed video captions, whereas inference often relies on user prompts rewritten by a prompt enhancer (PE). Prior work has improved generation by optimizing the PE, the DiT, or both; some methods have also sought to narrow the training-inference mismatch through shared schemas. Yet even within a shared schema, inference-time PE outputs and DiT training captions may still differ in detail selection, information organization, descriptive granularity, and phrasing. We refer to this residual mismatch as the PE-Caption gap and introduce CAPE-T2V, a two-step Captioner-Anchored Prompt Enhancement framework toward two-sided conditioning alignment in T2V generation. First, CAPE-T2V constructs three types of PE training examples, pairing captioner-generated targets with concise source captions, detailed source captions, or pseudo user prompts derived from those targets. It then fine-tunes the PE to map each input to its paired target. Second, CAPE-T2V fine-tunes the DiT on video-derived captions rewritten by the Anchored PE; the same PE rewrites user prompts at inference. Relative to a baseline using the same caption schema, CAPE-T2V achieves higher aggregate scores on StoryEval, VBench-2.0, and T2V-CompBench across Wan2.2 and LTX-2.3. Further, CAPE-T2V exhibits a smaller PE-Caption gap than the baseline: its DiT fine-tuning captions are closer in distribution to inference-time PE outputs, as measured by squared maximum mean discrepancy in a fixed embedding space. Overall, these results support CAPE-T2V as an effective approach to mitigating the PE-Caption gap. The project is available at https://github.com/yizzz927/CAPE-T2V.

Figures

Figures reproduced from arXiv: 2608.03046 by the authors.

Figure 1
Figure 1. Partial alignment is not enough. A StoryEval prompt requests three consecutive events. Conditioning the Original DiT directly on the user prompt completes none of them. The Anchored PE with the Original DiT and with the Schema-Aligned DiT each recovers a different single event. Only CAPE-T2V—where the same Anchored PE both writes the DiT’s fine-tuning captions and rewrites the user prompt at inference—completes all … view at source ↗
Figure 2
Figure 2. CAPE-T2V overview and matched comparison. In Step 1, the PE is trained on concise source captions, detailed source captions, and pseudo user prompts paired with captioner-generated targets, yielding the Anchored PE. In Step 2, captions produced by the Anchored PE are used to fine-tune the PE-Aligned DiT. For the matched comparison, the Schema-Aligned DiT is instead fine-tuned on captions produced by the prompted sch… view at source ↗
Figure 3
Figure 3. Four dimensions of residual conditioning mismatch within a shared schema. Starting from the same video-derived dense caption, the prompted rewriter produces the caption for the Schema-Aligned DiT, whereas the Anchored PE produces the caption for the PE-Aligned DiT. The paired example shows how the two caption operators can differ in detail selection, information organization, descriptive granularity, and phrasing de… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Qualitative projection of the three text distributions. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Representative DiT fine-tuning video. Eleven frames sampled at 2 fps show the multi-event sequence; labels give [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: Ordered multi-event execution (I). Each model strip is cropped from the corresponding row of the original composite and labeled explicitly. In (a), None reverses the insertion sequence, Anchored PE omits the initially closed tray state, and Schema￾Aligned DiT introduce…
Figure 7
Figure 7. Figure 7: Ordered multi-event execution (II). In (c), None omits the mouse, Anchored PE stops after the reveal, and Schema￾Aligned DiT is less stable around capture and consumption; CAPE-T2V provides the broadest coverage, though the final capture and eating remain visually ambi…
Figure 8
Figure 8. Figure 8: Dynamic state and trajectory control (I). In (e), None keeps the reflection static, while Anchored PE and Schema￾Aligned DiT let the second figure enter the room; CAPE-T2V keeps the action in the mirror and ends with an empty reflection. In (f), None is nearly static, …
Figure 9
Figure 9. Figure 9: Dynamic state and trajectory control (II). In (g), None shows little scale change, Anchored PE confounds scale with posture, and Schema-Aligned DiT weakens body consistency; CAPE-T2V gives the smoothest large-to-small trajectory while retaining identity. In (h), None u…
Figure 10
Figure 10. Figure 10: Interaction and subject–object binding (I). In (i), None obscures the hat transfer, Anchored PE separates the eagle and hat, and Schema-Aligned DiT lacks a stable carried endpoint; CAPE-T2V makes the removal, throw, approach, and final eagle–hat binding most visible. …
Figure 11
Figure 11. Figure 11: Interaction and subject–object binding (II). In (k), all methods produce a recognizable ping-pong scene, so the useful comparison is fine-grained continuity: Anchored PE and Schema-Aligned DiT more often lose the ball or hand–paddle relation, while CAPE-T2V keeps the …

Discussion (0). Continue with ORCID to comment.

Reference graph

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    Dense Caption:On a paved residential street lined with beige and red houses and parked cars, a man in a grey t-shirt and dark shorts dribbles a white soccer ball. A boy in a black t-shirt and jeans approaches him, followed by a secondboyinablueandredlong-sleevedshirtrunningfro...

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    Main Object Caption:The main subjects are a man in a grey t-shirt dribbling a white soccer ball and three boys who approach from the left and right, attempt to challenge him, and fall to the ground

  24. [36]

    Background Caption:The background features a paved residential street lined with beige and red houses and parked cars

  25. [37]

    Camera Caption:The camera maintains a wide, static shot throughout the sequence

  26. [38]

    A man ... dribbles ... while three boys attempt to tackle him and se- quentially fall

    Style Caption:The visual presentation stays focused on the described subjects and actions. Both captions are complete outputs under the same six- part schema. The Anchored PE caption moves the camera specification into the global summary and groups the entry directions, ordere...

  27. [42]

    ScoresaverageallninePE-conditionedvideosorthreeNone videos

    At evaluation, PE sampling seeds 42, 666, and 888 are crossed with DiT latent-noise seeds 42, 666, and 888 to producethe3×3grid;Noneusesthethreelatent-noiseseeds. ScoresaverageallninePE-conditionedvideosorthreeNone videos. For an Official PE, only the stochastic seed changes; ...

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    McInnes, L.; Healy, J.; and Melville, J

    POS: A Prompts Optimization Suite for Augmenting Text-to-VideoGeneration.arXivpreprintarXiv:2311.00949. McInnes, L.; Healy, J.; and Melville, J. 2018. UMAP: Uni- formManifoldApproximationandProjectionforDimension Reduction.arXiv preprint arXiv:1802.03426. MovieGenTeam.2024. Mo...

    2018 arXiv

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    InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, 15636–15645

    VPO:AligningText-to-VideoGenerationModelswith Prompt Optimization. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, 15636–15645. Du,Y.;Lin,Z.;Song,K.;Wang,B.;Zheng,Z.;Ge,T.;Zheng, B.; and Jin, Q. 2025. VC4VG: Optimizing Video Captions for Text-to-Vi...

    2025 arXiv

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    Wang, Q.; Shi, Y.; Ou, J.; Chen, R.; Lin, K.; Wang, J.; Jiang, B.; Yang, H.; Zheng, M.; Tao, X.; Yang, F.; Wan, P.; and Zhang, D

    PromptRL: Prompt Matters in RL for Flow-Based Image Generation.arXiv preprint arXiv:2602.01382. Wang, Q.; Shi, Y.; Ou, J.; Chen, R.; Lin, K.; Wang, J.; Jiang, B.; Yang, H.; Zheng, M.; Tao, X.; Yang, F.; Wan, P.; and Zhang, D. 2025a. Koala-36M: A Large-scale Video Dataset Impro...

    2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.