Update README.md

README.md

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-license: mit
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+<div align="center">
+
+#  MarsRL
+
+<div>
+   Advancing <strong>M</strong>ulti-<strong>A</strong>gent <strong>R</strong>easoning <strong>S</strong>ystem via <strong>R</strong>einforcement <strong>L</strong>earning with Agentic Pipeline Parallelism
+</div>
+</div>
+
+## Overview
+
+Recent progress in large language models (LLMs) has been propelled by reinforcement learning with verifiable rewards (RLVR) and test-time scaling. However, the limited output length of LLMs constrains the depth of reasoning attainable in a single inference process. Multi-agent reasoning systems offer a promising alternative by employing multiple agents including Solver, Verifier, and Corrector, to iteratively refine solutions. While effective in closed-source models like Gemini 2.5 Pro, they struggle to generalize to open-source models due to insufficient critic and correction capabilities. To address this, we propose MarsRL, a novel reinforcement learning framework with agentic pipeline parallelism, designed to jointly optimize all agents in the system. MarsRL introduces agent-specific reward mechanisms to mitigate reward noise and employs pipeline-inspired training to enhance efficiency in handling long trajectories. Applied to Qwen3-30B-A3B-Thinking-2507, MarsRL improves AIME2025 accuracy from 85.6\% to 93.3\% and BeyondAIME from 65.3\% to 72.6\%, even surpassing Qwen3-235B-A22B-Thinking-2507. These findings highlight the potential of MarsRL to advance multi-agent reasoning systems and broaden their applicability across diverse reasoning tasks.
+<div align="center">
+<img src="home.jpg" width="80%" />
+</div>
+
+## V-C Reasoning System Evaluation Instructions
+### step1: Download our released model or other open source models
+Supported models: Qwen3/DeepSeekV3.1/DeepSeek R1. You can modify the llm_client.py to use other models.
+
+### step2: Deploy service via VLLM
+
+### step3: Run the V-C reasoning system by the following commands:
+```
+python3 vc_reasoning_system.py solver_ip_port_1,solver_ip_port_2,... vc_ip_port_1,vc_ip_port_2,... test_file output_dir
+for example: python3 vc_reasoning_system.py 8.8.8.8:8021,12.34.56.78:8021 8.8.8.8:8021,12.34.56.78:8021 ./outputs/debug ./test_corpus/aime2025.jsonl
+```
+This step will run the reasoning system for each problem in the given $test_file$, the predicted results can be found in the output_dir
+
+### step4: Extract final solutions by the following commands:
+```
+python3 extract_solution.py result_dir test_file
+for example: python3 extract_solution.py ./outputs/debug ./test_corpus/aime_2025.jsonl
+```
+This step will generate a file named "eval_overalljsonl" in the input_dir. Your can evaluate the metrics based on this file.
+
+
+## Acknowledgements
+- Our implementation is heaviliy built on [verl](https://github.com/volcengine/verl).
+- Our models are trained on top of [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507).
+- Our V-C Reasoning system is built on [IMO25 pipline](https://github.com/lyang36/IMO25).
+  
+Thanks for their wonderful work.
+
+## Citation
+```bibtex
+TODO
+```