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rlhf
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
skills/rlhf/SKILL.md
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SKILL.mdcore · 14,189 bytesreference/direct-alignment.mddoc · 9,221 bytesreference/policy-optimization.mddoc · 10,470 bytesreference/reward-modeling.mddoc · 7,884 bytes
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# Understanding RLHF
Reinforcement Learning from Human Feedback (RLHF) is a technique for aligning language models with human preferences. Rather than relying solely on next-token prediction, RLHF uses human judgment to guide model behavior toward helpful, harmless, and honest outputs.
## Table of Contents
- [Core Concepts](#core-concepts)
- [The RLHF Pipeline](#the-rlhf-pipeline)
- [Preference Data](#preference-data)
- [Instruction Tuning](#instruction-tuning)
- [Reward Modeling](#reward-modeling)
- [Policy Optimization](#policy-optimization)
- [Direct Alignment Algorithms](#direct-alignment-algorithms)
- [Challenges](#challenges)