Initial commit

Author: ChenYi99

.gitignore

@@ -0,0 +1,11 @@
+**/__pycache__
+**/.ipynb_checkpoints
+model_checkpoints
+**/local*
+local*
+*ckpt
+.idea
+**/*.bin
+**/*.log
+**/eval*.json*
+wandb

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -0,0 +1,143 @@
+
+<div align="center">
+<h1>
+GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning
+
+<a href='https://arxiv.org/pdf/2503.24376'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> 
+<a href='https://huggingface.co/datasets/TencentARC/SEED-Bench-R1'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue'></a>
+<a href='https://huggingface.co/datasets/TencentARC/SEED-Bench-R1'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-green'></a>
+</h1>
+
+
+<p align="center">
+  <img src="./assets/teaser.png" width="100%" height="100%" />
+</p>
+
+</div>
+
+## 🚀Introduction
+
+Recent reinforcement learning (RL) approaches, such as outcome-supervised GRPO, have advanced Chain-of-Thought reasoning in large language models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) remains unexplored. 
+To address the lack of rigorous evaluation for MLLM post-training methods—especially on tasks requiring balanced perception and reasoning—we present **SEED-Bench-R1**, a benchmark featuring complex real-world videos that demand intricate visual understanding and commonsense planning. SEED-Bench-R1 uniquely provides a large-scale training set and evaluates generalization across three escalating challenges: in-distribution, cross-environment, and cross-environment-task scenarios.
+Using SEED-Bench-R1, we identify a key limitation of standard outcome-supervised GRPO: while it improves answer accuracy, it often degrades the logical coherence between reasoning steps and final answers, achieving only a 57.9\% consistency rate. We attribute this to (1) reward signals focused solely on final answers, which encourage shortcut solutions at the expense of reasoning quality, and (2) strict KL divergence penalties, which overly constrain model exploration and hinder adaptive reasoning.
+
+To overcome these issues, we propose **GRPO-CARE**, a novel consistency-aware RL framework that jointly optimizes for both answer correctness and reasoning coherence, without requiring explicit process supervision. GRPO-CARE introduces a two-tiered reward: (1) a base reward for answer correctness, and (2) an adaptive consistency bonus, computed by comparing the model’s reasoning-to-answer likelihood (via a slowly-evolving reference model) against group peers. This dual mechanism amplifies rewards for reasoning paths that are both correct and logically consistent.
+By replacing the KL penalty with an adaptive, group-relative consistency bonus, GRPO-CARE consistently outperforms standard GRPO on SEED-Bench-R1, achieving a 6.7\% performance gain on the most challenging evaluation level and a 24.5\% improvement in consistency rate. Furthermore, GRPO-CARE demonstrates strong transferability, improving model performance across diverse video understanding benchmarks.
+Our work contributes a systematically designed benchmark and a generalizable post-training framework, advancing the development of more interpretable and robust MLLMs.
+
+## 🚩News
+- [2025/06/18] We release the training code for GRPO-CARE💥
+- [2025/03/31] We release the datasets of [SEED-Bench-R1](https://github.com/TencentARC/SEED-Bench-R1/) and the training / evaluation codes.
+
+## 📝Data
+
+SEED-Bench-R1 consists of a large-scale training set and a hierarchical three-level validation set for in-distribution, cross-environment, and cross-environment-task evaluations. The datasets can be downloaded from [HuggingFace](https://huggingface.co/datasets/TencentARC/SEED-Bench-R1). 
+
+Specifically, SEED-Bench-R1 is built on our prior works, reusing the training and validation data from our [EgoPlan-Bench](https://github.com/ChenYi99/EgoPlan), as well as the test data from our [EgoPlan-Bench2](https://github.com/qiulu66/EgoPlan-Bench2). The validation data from EgoPlan-Bench are used for Level-1 (in-distribution) and Level-2 (OOD, cross-environment) evaluation, while the test data from EgoPlan-Bench2 cover more general domains and are used for Level-3 (OOD, cross-environment-task) evaluation.
+
+<p align="center">
+  <img src="./assets/data_statistics.png" width="100%" height="100%" />
+</p>
+
+Questions from the human-verified validation data are formatted as multiple-choice problems. MLLMs need to select the most reasonable answer from four candidate choices. The primary metric is Accuracy. 
+
+<p align="center">
+  <img src="./assets/question_examples.png" width="100%" height="100%" />
+</p>
+
+
+
+## 🔥Training Models
+
+> [!NOTE] The training commands below are configured for a node of 4 x 80GPUs. For different hardware and topologies, you may need to tune the batch size and number of gradient accumulation steps.
+
+### Set Up
+```
+git clone https://github.com/TencentARC/GRPO-CARE.git
+cd GRPO-CARE
+
+# build environment
+conda create -n grpo-care python=3.11 
+conda activate grpo-care
+bash setup.sh
+
+# download SEED-Bench-R1 data
+git lfs install
+git clone https://huggingface.co/datasets/TencentARC/SEED-Bench-R1
+
+# download the model checkpoint of Qwen2.5-VL-7B-Instruct
+git clone https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct
+
+# download Video-R1 data [optional for training or evaluation on general video understanding benchmarks]
+cd ./src/r1-v/
+git clone https://huggingface.co/datasets/Video-R1/Video-R1-data
+python ./src/unzip.py
+```
+
+### Training on SEED-Bench-R1 Data
+
+For evaluation on SEED-Bench-R1, please use [run_grpo_care_margin0_seed_bench_r1.sh](scripts/run_grpo_care_margin0_seed_bench_r1.sh).
+
+For evaluation on general video understanding benchmarks, please use [run_grpo_care_margin0.01_seed_bench_r1.sh](scripts/run_grpo_care_margin0.01_seed_bench_r1.sh)
+
+> We found that training the model on SEED-Bench-R1 data using GRPO-CARE with a consistency_margin of 0.01 improves its performance on general video understanding benchmarks, while setting the consistency_margin to 0 yields better results on SEED-Bench-R1 itself.
+
+### Training on General-Domain Data ([Video-R1-260k](https://huggingface.co/datasets/Video-R1/Video-R1-data/blob/main/Video-R1-260k.json))
+
+Please refer to [run_grpo_care_margin0.01_video_r1.sh](scripts/run_grpo_care_margin0.01_video_r1.sh).
+
+
+
+## 🤖Evaluating Models
+
+### Evaluation on SEED-Bench-R1
+
+```
+export CUDA_VISIBLE_DEVICES=0
+export PROJECT_ROOT=[your path to grpo-care root]
+conda activate grpo-care
+cd ${PROJECT_ROOT}
+nohup bash scripts/eval_general_video_bench.sh > scripts/eval_general_video_bench.log 2>&1 &
+tail -f scripts/eval_general_video_bench.log
+```
+
+
+### Evaluation on General Video Understanding Benchmarks
+
+```
+export CUDA_VISIBLE_DEVICES=0
+export PROJECT_ROOT=[your path to grpo-care root]
+conda activate grpo-care
+cd ${PROJECT_ROOT}
+nohup bash scripts/eval_general_video_bench.sh > scripts/eval_general_video_bench.log 2>&1 &
+tail -f scripts/eval_general_video_bench.log
+```
+
+## 🙌References & Acknowledgements
+We sincerely thank the contributions from the open source community. The related projects are as follows:
+- [SEED-Bench-R1](https://github.com/TencentARC/SEED-Bench-R1)
+- [Video-R1](https://github.com/tulerfeng/Video-R1)
+- [Open-R1-Video](https://github.com/Wang-Xiaodong1899/Open-R1-Video)
+- [EgoPlan](https://github.com/ChenYi99/EgoPlan)
+- [EgoPlan-Bench2](https://github.com/qiulu66/EgoPlan-Bench2)
+- [open-r1-multimodal](https://github.com/EvolvingLMMs-Lab/open-r1-multimodal)
+- [lmm-r1](https://github.com/TideDra/lmm-r1)
+- [DeepSeek](https://github.com/deepseek-ai/DeepSeek-R1) 
+- [Open-R1](https://github.com/huggingface/open-r1)
+- [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF)
+
+## ⭐License
+The video samples in SEED-Bench-R1 are collected from [Epic-Kitchens](https://epic-kitchens.github.io/2025) and [Ego4D](https://ego4d-data.org/). Users must follow the related licenses ([Epic-Kitchens](https://creativecommons.org/licenses/by-nc/4.0/) and [Ego4D](https://ego4ddataset.com/ego4d-license/)) to use these video samples for training and validation. SEED-Bench-R1 does not hold the copyright for these videos and the copyright belongs to the original owner of these datasets.
+
+
+## 📚Citation
+If you find our project helpful, hope you can star our repository and cite our paper as follows:
+
+```bibtex
+@article{chen2025exploring,
+  title={Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1},
+  author={Chen, Yi and Ge, Yuying and Wang, Rui and Ge, Yixiao and Qiu, Lu and Shan, Ying and Liu, Xihui},
+  journal={arXiv preprint arXiv:2503.24376},
+  year={2025}
+}
+```