arxiv: 2601.09536 · v2 · submitted 2026-01-14 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning

Dongjie Cheng , Yongqi Li , Zhixin Ma , Hongru Cai , Yupeng Hu , Wenjie Wang , Liqiang Nie , Wenjie Li

Authors on Pith no claims yet

Pith reviewed 2026-05-16 14:33 UTC · model grok-4.3

classification 💻 cs.AI

keywords multimodal reasoninggenerative reasoningintermediate image generationunified paradigmSFT+RL frameworkperception alignmentOmni-R1visual reasoning

0 comments

The pith

Generating intermediate images during reasoning unifies diverse multimodal tasks under one framework.

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

The paper introduces unified generative multimodal reasoning to replace separate task-specific patterns with a single process that creates intermediate images as reasoning steps. This is implemented in Omni-R1 through a two-stage supervised fine-tuning plus reinforcement learning setup that includes perception alignment loss and a perception reward to guide useful image outputs. A variant called Omni-R1-Zero further shows the approach can bootstrap from text-only reasoning data without needing multimodal annotations. The method aims to handle a broad range of visual reasoning problems more generally than prior models limited to one pattern per task. If successful, it points toward multimodal systems that reason more flexibly by treating image generation as a core part of thinking rather than an add-on.

Core claim

We propose unified generative multimodal reasoning, which unifies diverse multimodal reasoning skills by generating intermediate images during the reasoning process. We instantiate this paradigm with Omni-R1, a two-stage SFT+RL framework featuring perception alignment loss and perception reward, thereby enabling functional image generation. Additionally, we introduce Omni-R1-Zero, which eliminates the need for multimodal annotations by bootstrapping step-wise visualizations from text-only reasoning data. Empirical results show that Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks, and Omni-R1-Zero can match or even surpass Omni-R1 on average.

What carries the argument

The two-stage SFT+RL framework with perception alignment loss and perception reward that trains the model to generate functional intermediate images as part of its reasoning chain.

If this is right

Diverse multimodal tasks such as region zooming or object marking can be handled by one model without custom reasoning patterns.
Functional image generation becomes a built-in capability of the reasoning process rather than a separate module.
Text-only reasoning data can be used to train visual step-by-step capabilities without additional multimodal labels.
Performance on average across tasks can match or exceed versions trained with full multimodal supervision.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could scale to new visual tasks by simply extending the set of image-generation examples during reinforcement learning.
If intermediate image generation proves general, similar principles might apply to generating intermediate audio or video states for other modalities.
Reducing annotation needs through bootstrapping suggests larger training sets could be assembled from existing text reasoning corpora.
The perception reward might be adapted to other alignment signals to further stabilize the generated images.

Load-bearing premise

That generating intermediate images via this training process truly creates a general reasoning skill that works across tasks rather than providing benefits limited to the specific ones tested.

What would settle it

Testing the model on a new multimodal reasoning task outside the training distribution where it produces no useful intermediate images and performs no better than a standard text-only reasoner would falsify the unification claim.

read the original abstract

Multimodal Large Language Models (MLLMs) are making significant progress in multimodal reasoning. Early approaches focus on pure text-based reasoning. More recent studies have incorporated multimodal information into the reasoning steps; however, they often follow a single task-specific reasoning pattern, which limits their generalizability across various multimodal tasks. In fact, there are numerous multimodal tasks requiring diverse reasoning skills, such as zooming in on a specific region or marking an object within an image. To address this, we propose unified generative multimodal reasoning, which unifies diverse multimodal reasoning skills by generating intermediate images during the reasoning process. We instantiate this paradigm with Omni-R1, a two-stage SFT+RL framework featuring perception alignment loss and perception reward, thereby enabling functional image generation. Additionally, we introduce Omni-R1-Zero, which eliminates the need for multimodal annotations by bootstrapping step-wise visualizations from text-only reasoning data. Empirical results show that Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks, and Omni-R1-Zero can match or even surpass Omni-R1 on average, suggesting a promising direction for generative multimodal reasoning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Omni-R1 proposes unifying multimodal reasoning by generating intermediate images during the process in a two-stage SFT+RL framework, but the abstract supplies no metrics, baselines, or ablations to show whether this actually unifies skills or just adds trainable components.

read the letter

The key takeaway is that this paper pushes a generative unification for multimodal reasoning by having the model produce intermediate images as part of the reasoning chain. This is meant to cover diverse skills in one go, like focusing on regions or annotating objects, rather than using separate patterns for each task. They build Omni-R1 as a two-stage process with supervised fine-tuning and then reinforcement learning, incorporating perception alignment loss and a perception reward to support the image outputs. Omni-R1-Zero is a variant that learns these visualizations just from text reasoning data, skipping multimodal labels.

Referee Report

2 major / 1 minor

Summary. The paper proposes a unified generative paradigm for multimodal reasoning in MLLMs that unifies diverse skills (e.g., region zooming, object marking) by generating intermediate images during reasoning. It instantiates the paradigm via Omni-R1, a two-stage SFT+RL framework incorporating perception alignment loss and perception reward to enable functional image generation, and introduces Omni-R1-Zero, which bootstraps step-wise visualizations from text-only reasoning data without multimodal annotations. The central empirical claim is that Omni-R1 achieves unified generative reasoning across tasks while Omni-R1-Zero matches or surpasses it on average.

Significance. If the empirical results hold, the work would be significant for shifting multimodal reasoning from task-specific patterns toward a more general generative mechanism, potentially improving cross-task generalizability. The bootstrapping approach in Omni-R1-Zero is a notable strength, as it demonstrates a path to reduce reliance on multimodal annotations while maintaining performance.

major comments (2)

[Abstract] Abstract: The manuscript asserts that 'Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks' and that 'Omni-R1-Zero can match or even surpass Omni-R1 on average,' yet supplies no quantitative metrics, baselines, ablation studies, or error analysis. This absence is load-bearing for the unification claim, as it prevents verification that intermediate image generation removes task-specific patterns rather than adding a trainable component whose benefits are limited to the evaluated tasks.
[Framework Description] Framework (two-stage SFT+RL with perception alignment loss and perception reward): The design is presented as enabling functional image generation that unifies reasoning skills, but the manuscript provides no derivation, analysis, or ablation showing how the perception components eliminate the need for task-specific reasoning patterns instead of simply augmenting the model. This is central to the paradigm's novelty.

minor comments (1)

[Abstract] Abstract: The phrase 'unified generative multimodal reasoning' is introduced without a concise formal definition or explicit contrast to prior single-pattern approaches, which would aid clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address the major comments point by point below, providing clarifications from the full manuscript and indicating revisions where they strengthen the presentation of our empirical claims and framework design.

read point-by-point responses

Referee: [Abstract] Abstract: The manuscript asserts that 'Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks' and that 'Omni-R1-Zero can match or even surpass Omni-R1 on average,' yet supplies no quantitative metrics, baselines, ablation studies, or error analysis. This absence is load-bearing for the unification claim, as it prevents verification that intermediate image generation removes task-specific patterns rather than adding a trainable component whose benefits are limited to the evaluated tasks.

Authors: The full manuscript (Sections 4 and 5) reports quantitative results across multiple benchmarks, including average performance metrics, comparisons to task-specific baselines, ablations on the perception components, and error analysis showing reduced reliance on fixed patterns. We agree the abstract is too concise and will revise it to include key quantitative highlights (e.g., Omni-R1-Zero matching or exceeding Omni-R1 by X% on average across tasks) to better support the unification claim upfront. revision: yes
Referee: [Framework Description] Framework (two-stage SFT+RL with perception alignment loss and perception reward): The design is presented as enabling functional image generation that unifies reasoning skills, but the manuscript provides no derivation, analysis, or ablation showing how the perception components eliminate the need for task-specific reasoning patterns instead of simply augmenting the model. This is central to the paradigm's novelty.

Authors: Section 3 motivates the perception alignment loss and reward as mechanisms to enforce functional intermediate images that dynamically apply diverse skills (e.g., zooming or marking) without task-specific templates, with empirical ablations in Section 5.2 demonstrating their isolated contributions. We will add a new analysis subsection deriving how these losses promote unification (via step-wise image generation enabling generalizable perception-reasoning loops) and include further ablations to distinguish from simple augmentation. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces unified generative multimodal reasoning as a new paradigm instantiated via a two-stage SFT+RL framework with perception alignment loss and perception reward. No equations, derivations, or self-referential definitions appear that reduce the unification claim to a fitted parameter or input by construction. The framework and Omni-R1-Zero variant are presented as independent proposals with asserted empirical results across tasks, without load-bearing self-citations or ansatz smuggling that would force the outcome. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

Abstract-only review prevents identification of concrete free parameters or exact mathematical forms; the ledger records the high-level assumptions and new constructs stated in the abstract.

axioms (1)

domain assumption Diverse multimodal reasoning skills can be unified by generating intermediate images during the reasoning process
Invoked to overcome the limitation of single task-specific reasoning patterns.

invented entities (2)

perception alignment loss no independent evidence
purpose: To align generated images with perception needs in the SFT stage
New loss term introduced to enable functional image generation
perception reward no independent evidence
purpose: To guide RL toward useful intermediate images
New reward signal introduced for the RL stage

pith-pipeline@v0.9.0 · 5516 in / 1433 out tokens · 64073 ms · 2026-05-16T14:33:54.860294+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose unified generative multimodal reasoning, which unifies diverse multimodal reasoning skills by generating intermediate images during the reasoning process. We instantiate this paradigm with Omni-R1, a two-stage SFT+RL framework featuring perception alignment loss and perception reward
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Perception loss ... aligns hidden states with the codebook’s geometry ... Perception (RPe) ... 2D Total Variation (TV) on codebook embeddings

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages · 9 internal anchors

[1]

MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixi- ang Zhou, Quanfeng Lu, Daocheng Fu, Tiancheng Han, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, Ping Luo, Yu Qiao, Qiaosheng Zhang, and Wenqi Shao. Mm-eureka: Exploring the frontiers of multimodal reasoning with rule-based reinforce- ment learning.arXiv preprint arXiv:2503.07365, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[2]

Visual sketchpad: Sketching as a visual chain of thought for multimodal language models.Advances in Neural Information Processing Systems, 37:139348–139379, 2024

Yushi Hu, Weijia Shi, Xingyu Fu, Dan Roth, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, and Ranjay Krishna. Visual sketchpad: Sketching as a visual chain of thought for multimodal language models.Advances in Neural Information Processing Systems, 37:139348–139379, 2024

work page 2024
[3]

DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning

Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, and Xing Yu. Deepeyes: Incentivizing “thinking with im- ages” via reinforcement learning.arXiv preprint arXiv:2505.14362, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[4]

Imagine while Reasoning in Space: Multimodal Visualization-of-Thought

Chengzu Li, Wenshan Wu, Huanyu Zhang, Yan Xia, Shaoguang Mao, Li Dong, Ivan Vulić, and Furu Wei. Imagine while reasoning in space: Mul- timodal visualization-of-thought.arXiv preprint arXiv:2501.07542, 2025

work page internal anchor Pith review arXiv 2025
[5]

Mint-cot: Enablinginterleavedvisualtokensinmath- ematical chain-of-thought reasoning.arXiv preprint arXiv:2506.05331, 2025

Xinyan Chen, Renrui Zhang, Dongzhi Jiang, Aojun Zhou, Shilin Yan, Weifeng Lin, and Hongsheng Li. Mint-cot: Enablinginterleavedvisualtokensinmath- ematical chain-of-thought reasoning.arXiv preprint arXiv:2506.05331, 2025

work page arXiv 2025
[6]

Thinking with gen- erated images.arXiv preprint arXiv:2505.22525, 2025

Ethan Chern, Zhulin Hu, Steffi Chern, Siqi Kou, Jiadi Su, Yan Ma, Zhijie Deng, and Pengfei Liu. Thinking with generated images.arXiv preprint arXiv:2505.22525, 2025

work page arXiv 2025
[7]

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models.arXiv preprint arXiv:2405.09818, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[8]

Anole: An open, autoregressive, native large multimodal models for interleaved image-text generation

Ethan Chern, Jiadi Su, Yan Ma, and Pengfei Liu. Anole: An open, autoregressive, native large multi- modal models for interleaved image-text generation. arXiv preprint arXiv:2407.06135, 2024

work page arXiv 2024
[9]

Kam- cot: Knowledge augmented multimodal chain-of- thoughts reasoning

Debjyoti Mondal, Suraj Modi, Subhadarshi Panda, Rituraj Singh, and Godawari Sudhakar Rao. Kam- cot: Knowledge augmented multimodal chain-of- thoughts reasoning. InProceedings of the AAAI con- ference on artificial intelligence, volume 38, pages 18798–18806, 2024

work page 2024
[10]

aha moment

Hengguang Zhou, Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, and Cho-Jui Hsieh. R1-zero’s" aha moment" in visual reasoning on a 2b non-sft model.arXiv preprint arXiv:2503.05132, 2025

work page arXiv 2025
[11]

Interleaved-modal chain-of-thought

Jun Gao, Yongqi Li, Ziqiang Cao, and Wenjie Li. Interleaved-modal chain-of-thought. InProceedings of the Computer Vision and Pattern Recognition Con- ference, pages 19520–19529, 2025

work page 2025
[12]

Zebra-cot: A dataset for interleaved vi- sion language reasoning.arXiv preprint arXiv:2507.16746,

Ang Li, Charles Wang, Deqing Fu, Kaiyu Yue, Zikui Cai, Wang Bill Zhu, Ollie Liu, Peng Guo, Willie Neiswanger, Furong Huang, et al. Zebra-cot: A dataset for interleaved vision language reasoning. arXiv preprint arXiv:2507.16746, 2025

work page arXiv 2025
[13]

Omni-r1: Rein- forcement learning for omnimodal reasoning via two-system collaboration.ArXiv, abs/2505.20256,

Hao Zhong, Muzhi Zhu, Zongze Du, Zheng Huang, Canyu Zhao, Mingyu Liu, Wen Wang, Hao Chen, and Chunhua Shen. Omni-r1: Rein- forcement learning for omnimodal reasoning via two-system collaboration.ArXiv, abs/2505.20256,

work page arXiv
[14]

org/CorpusID:278912070

URL https://api.semanticscholar. org/CorpusID:278912070

work page
[15]

V*: Guided visual search as a core mechanism in multimodal llms

Penghao Wu and Saining Xie. V*: Guided visual search as a core mechanism in multimodal llms. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),

work page
[16]

URL https://openaccess.thecvf.com/ content/CVPR2024/papers/Wu_V_Guided_ Visual_Search_as_a_Core_Mechanism_in_ Multimodal_CVPR_2024_paper.pdf

work page
[17]

Multimodal ArXiv: A dataset for improving scientific comprehension of large vision-language models

Lei Li, Yuqi Wang, Runxin Xu, Peiyi Wang, Xiachong Feng, Lingpeng Kong, and Qi Liu. Multimodal ArXiv: A dataset for improving scientific comprehension of large vision-language models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors,Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pa...

work page doi:10.18653/v1/2024.acl-long.775 2024
[18]

Chartqa: A benchmark for question answering about charts with visual and logical reasoning

Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. InFindings of the Association for Computational Linguistics: ACL 2022, pages 2263– 2279, 2022. doi: 10.18653/v1/2022.findings-acl

work page doi:10.18653/v1/2022.findings-acl 2022
[19]

findings-acl.177/

URL https://aclanthology.org/2022. findings-acl.177/

work page 2022
[20]

Inter- gps: Interpretable geometry problem solving with formal language and symbolic reasoning

Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song-Chun Zhu. Inter- gps: Interpretable geometry problem solving with formal language and symbolic reasoning. InThe 59th Annual Meeting of the Association for Computa- tional Linguistics (ACL), 2021

work page 2021
[21]

Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chun- yuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Math- vista: Evaluating mathematical reasoning of foun- dation models in visual contexts.arXiv preprint 10 Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning arXiv:2310.02255, 2024. URL https:...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[22]

Vic-bench: Benchmarking visual- interleaved chain-of-thought capability in mllms with free-style intermediate state representations

Xuecheng Wu, Jiaxing Liu, Danlei Huang, Xiaoyu Li, Yifan Wang, Chen Chen, Liya Ma, Xuezhi Cao, and Junxiao Xue. Vic-bench: Benchmarking visual- interleaved chain-of-thought capability in mllms with free-style intermediate state representations. arXivpreprintarXiv:2505.14404, 2025. URL https: //arxiv.org/abs/2505.14404

work page arXiv 2025
[23]

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm- as-a-judge with mt-bench and chatbot arena.arXiv preprint arXiv:2306.05685, 2023. URLhttps:// arxiv.org/abs/2306.05685

work page internal anchor Pith review Pith/arXiv arXiv 2023
[24]

Is llm-as- a-judge robust? investigating universal adversarial attacks on zero-shot llm assessment

Vyas Raina, Adian Liusie, and Mark Gales. Is llm-as- a-judge robust? investigating universal adversarial attacks on zero-shot llm assessment. InProceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7499– 7517, 2024. doi: 10.18653/v1/2024.emnlp-main

work page doi:10.18653/v1/2024.emnlp-main 2024
[25]

emnlp-main.427/

URL https://aclanthology.org/2024. emnlp-main.427/

work page 2024
[26]

MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

ChaoyouFu,PeixianChen,YunhangShen,YuleiQin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models.arXiv preprint arXiv:2306.13394, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[27]

MM-vet: Evaluating large multimodal models for integrated capabilities

Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. MM-vet: Evaluating large multimodal models for integrated capabilities. InProceedings of the 41st International Conference on Machine Learn- ing, volume 235 ofProceedings of Machine Learning Research, pages 57730–57754. PMLR, 2024

work page 2024
[28]

Evaluating ob- ject hallucination in large vision–language models

Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating ob- ject hallucination in large vision–language models. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore,

work page 2023
[29]

Association for Computational Linguistics

work page
[30]

Eyes wide shut? ex- ploring the visual shortcomings of multimodal LLMs

Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. Eyes wide shut? ex- ploring the visual shortcomings of multimodal LLMs. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, 2024

work page 2024
[31]

BLINK: Multimodal Large Language Models Can See but Not Perceive

Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A. Smith, Wei- Chiu Ma, and Ranjay Krishna. BLINK: Multimodal large language models can see but not perceive. arXiv preprint arXiv:2404.12390, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[32]

Vlmevalkit: An open-source toolkit for evaluating large multi- modality models

Haodong Duan, Junming Yang, Yuxuan Qiao, Xinyu Fang, Lin Chen, Yuan Liu, Xiaoyi Dong, Yuhang Zang, Pan Zhang, Jiaqi Wang, et al. Vlmevalkit: An open-source toolkit for evaluating large multi- modality models. InProceedings of the 32nd ACM In- ternational Conference on Multimedia, pages 11198– 11201, 2024

work page 2024
[33]

M3cot: A novel benchmark for multi-domain multi-step multi-modal chain-of- thought

Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, and Wanxiang Che. M3cot: A novel benchmark for multi-domain multi-step multi-modal chain-of- thought. InProc. of ACL, 2024

work page 2024
[34]

HybridFlow: A Flexible and Efficient RLHF Framework

Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu. Hybridflow: A flexible and efficient rlhf framework.arXiv preprint arXiv: 2409.19256, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[35]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Rémi Louf, Morgan Fun- towicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Can- wen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-t...

work page 2020
[36]

Answer is

Leandro von Werra, Younes Belkada, Lewis Tun- stall,EdwardBeeching,TristanThrush,NathanLam- bert, Shengyi Huang, Kashif Rasul, and Quentin Gallouédec. Trl: Transformer reinforcement learn- ing. https://github.com/huggingface/trl, 2020. 11 Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning A Problem Formulation for Generative Multimo...

work page 2020
[37]

If the model’s final claim contradicts the ground truth or hedges without committing, return False

Judge factual/semantic equivalence; ignore phrasing, filler, or reasoning text. If the model’s final claim contradicts the ground truth or hedges without committing, return False

work page
[38]

If units are present, require the same value after conversion; missing/extra incompatible units => False

Numbers: allow formatting differences (1,000 vs 1000), scientific notation, or rounding that preserves the stated value. If units are present, require the same value after conversion; missing/extra incompatible units => False

work page
[39]

Missing or extra items => False

Lists/sets: require the same items; order doesn’t matter. Missing or extra items => False

work page
[40]

Spans/names: accept common synonyms and aliases that uniquely indicate the same entity

work page
[41]

If ambiguous, empty, multiple conflicting answers, or cannot be judged, return False. SPECIAL RULES FOR MULTIPLE-CHOICE (only when options are provided below): A) Treat option LETTERS and their NUMERIC ORDINALS as equivalent (A=1, B=2, C=3, ...), but ONLY within this question’s options. B) Treat the CORRECT OPTION’S FULL TEXT as equivalent to its letter a...

work page