Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
7.6
Rating
0
Installs
AI & LLM
Category
Excellent skill for LLM fine-tuning with reinforcement learning. The description clearly covers TRL's core capabilities (SFT, DPO, PPO, GRPO, reward modeling) and when to use them. Task knowledge is comprehensive with complete, runnable code for three major workflows (full RLHF pipeline, DPO alignment, GRPO training), proper dataset formats, troubleshooting, and CLI alternatives. Structure is clean with concise SKILL.md and references for advanced topics. Novelty is strong: orchestrating multi-step RLHF pipelines, configuring RL hyperparameters, and handling preference alignment would require significant tokens and domain expertise from a CLI agent alone. Minor improvement areas: could add more explicit decision trees for method selection and clearer hardware scaling guidance. Overall, this is a high-quality skill that meaningfully reduces complexity for AI alignment tasks.
zechenzhangAGI
Skill Author
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