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dengxianghua888-ops / ecoalign-forge

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Multi-Agent DPO Data Synthesis Factory — 多智能体偏好训练数据自动合成框架 | 红队攻击 → 多persona审核 → 终审裁决 → DPO偏好对

content-moderation data-quality dpo llm multi-agent
60
0
100% credibility
Found Apr 11, 2026 at 60 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

EcoAlign-Forge automatically generates preference data pairs for training AI content moderation models by simulating debates between specialized AI agents.

How It Works

1
🔍 Discover the data factory

You hear about EcoAlign-Forge, a clever tool that creates training examples for AI safety checkers by having smart helpers argue over tricky posts.

2
📥 Get it on your computer

Download and set it up easily, like installing any helpful app, so it's ready to use right away.

3
📝 Share your safety rules

Tell it simple rules about what to watch for, like hidden ads or low-quality junk, and it remembers them perfectly.

4
▶️ Start the factory

Hit go, and watch as AI helpers create test posts, review them like junior staff, and judge them like experts – all automatically.

5
📊 Check the live dashboard

Open a colorful screen showing charts of how well it's working, quality scores, and what's being caught.

6
💾 Grab your training data

It saves thousands of good-vs-bad example pairs, ready to teach your AI how to spot problems.

🎉 Train smarter AI faster

Now your content checker learns from reliable examples without waiting weeks or paying labelers – everything runs smoothly!

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AI-Generated Review

What is ecoalign-forge?

Ecoalign-forge is a Python multi-agent framework that synthesizes DPO preference data for training content moderation models. Feed it a safety policy like stealth marketing rules, and agents simulate red-team attacks, naive reviews, and expert judgments to produce thousands of chosen/rejected pairs with full traceability—no human annotators needed. Run `python -m ecoalign_forge --demo` for instant output in TRL or ShareGPT formats, using LiteLLM for any LLM provider.

Why is it gaining traction?

Unlike manual labeling at $0.5+ per pair, this multi-agent DPO factory delivers consistent data under $0.01/pair via agent debates that capture real disagreements. Standout hooks include a Streamlit dashboard for real-time KPIs, adaptive sampling for edge cases, and flywheel iterations to refine data over rounds—echoing claude multi agent github workflows but tuned for DPO synthesis. Exports plug straight into TRL trainers, with IAA metrics ensuring quality.

Who should use this?

ML engineers bootstrapping DPO/RLHF for social platforms need cold-start moderation data without weeks of annotation. Trust & Safety leads at scale-ups generating edge cases humans miss, like AI slop or hidden ads. AI researchers red-teaming LLMs via structured multi-agent orchestration.

Verdict

Grab it for quick DPO data factories if you're in alignment—demo proves the concept works. At 60 stars and 1.0% credibility, it's alpha-stage with solid tests and docs, but watch for production hardening before heavy lifts.

(187 words)

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