EIT-EAST-Lab

EIT-EAST-Lab / C3

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Official implementation of the paper "Contextual Counterfactual Credit Assignment for Multi-Agent Reinforcement Learning in LLM Collaboration". (by Yanjun Chen)

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Found Mar 10, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository is a research codebase implementing Contextual Counterfactual Credit Assignment (C3) for improving multi-agent reinforcement learning in collaborative LLM systems on math and coding benchmarks.

How It Works

1
🔍 Discover C3

You find this exciting project that helps AI teams work better together on tough math and coding problems, with a clear guide and paper to read.

2
📥 Get everything ready

Follow simple steps to download and set up the tools, data, and smart models needed for your experiments.

3
✅ Test it works

Run a quick check to see your AI team chatting and solving problems correctly right away.

4
🚀 Start training

Launch the training so your AI agents learn to collaborate smarter on math and code challenges.

5
📊 Check the results

Review charts and numbers showing how much better your AI team performs.

🎉 AI team succeeds

Celebrate as your collaborative AI agents solve problems more accurately and efficiently than before!

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AI-Generated Review

What is C3?

C3 implements contextual counterfactual credit assignment for multi-agent reinforcement learning in LLM collaborations, tackling credit diffusion across long trajectories in team-based tasks like math solving and code generation. By freezing transcript context and replaying leave-one-out alternatives with a centralized critic, it delivers sharper per-agent advantages than vanilla baselines. Python-based with PyTorch, OpenRLHF, Ray, and vLLM integration, users get CLI-driven reproduction of paper experiments via audited scripts—no bundled datasets or checkpoints to pollute your setup.

Why is it gaining traction?

Unlike generic MARL libs, C3 ties theory to code with implementation audits, release gates, and exact repro scripts for Pareto fronts and learning curves, making it dead simple to verify claims against MAPPO or MAGRPO. The vendored OpenRLHF stack scales to multi-GPU PPO training on Qwen models, while analysis tools quantify credit fidelity and agent influence. For C3 AI researchers hunting GitHub C3 frameworks, it's the official drop with official GitHub Actions lite CI.

Who should use this?

RLHF engineers tuning LLM teams for collaborative reasoning, like math provers or code agents. Ideal for academics reproducing the arXiv paper or devs benchmarking counterfactual credit in production pipelines—skip if you're not doing multi-agent PPO.

Verdict

Grab it if multi-agent LLM RL is your jam; repro scripts and docs make extension feasible despite 17 stars and 1.0% credibility score signaling early-stage polish. Low maturity means expect some glue code for your stack, but the audit transparency builds trust fast. (187 words)

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