The core idea
A model produces candidates, receives a reward signal and updates toward behavior earning higher rewards.
How it relates to reasoning
For checkable problems, rewards can encourage successful strategies and discourage unproductive ones.
Limits and misconceptions
Longer reasoning is not necessarily correct. Reward design can create shortcuts and benchmarks do not guarantee real-world safety.
- Rewards shape behavior
- Use unseen evaluations
- Keep human oversight
Use the official source
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Visit DeepSeek.com ↗Frequently asked questions
What is RLHF?
It uses human preference judgments to help shape model behavior.
Did DeepSeek invent RL?
No. Reinforcement learning is a long-established field.