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. Also covers GRPO with TRL and SimPO (guides in references/).
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tessl scenario generatetessl eval runOpenLAIR/dr-claw
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