NousResearch/atropos
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Added new env info

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Shannon Sands 2025-05-16 16:44:33 -07:00
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@ -52,10 +52,12 @@ The framework supports collecting, distributing and evaluating LLM trajectories
| Environment Type | Examples | Purpose |
|---------------------------|--------------------------------------------|----------------------------------------------------|
| 📚 Dataset environments | GSM8K, MMLU | Evaluate and improve LLM performance on static data|
| 🎮 Online environments | Crosswords, Hangman | Train LLMs through interactive game-based learning |
| 📚 Dataset environments | GSM8K, MMLU, Custom HF Datasets | Evaluate and improve LLM performance on static data|
| 🎮 Online environments | Blackjack, Taxi, Text-based games | Train LLMs through interactive game-based learning |
| 🤖 RLAIF and RLHF | LLM Judge/Reward Models | Fine-tune LLMs using human feedback and alignment |
| 🔄 Multi-Turn RL | deepresearch, internal tool calling | Train LLMs on complex multi-step interactions |
| 💻 Code Execution | MBPP, HumanEval (via `coding_server.py`) | Train LLMs to generate and execute code |
| 🖼️ Multimodal | OCR VQA, Clevr (via `multimodal_dpo/`) | Train LLMs on tasks involving vision and language |
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