# RLHF: Research Digest

> Reinforcement learning from human feedback (RLHF) has become a central alignment recipe for language models and interactive agents: humans rank or compare outputs, a reward…

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- **Updated:** 2026-06-15
- **Category:** scholar
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## Key data

- **Papers:** 10
- **Field:** reinforcement learning from human feedback
- **Updated:** 2026-06-15

Reinforcement learning from human feedback (**RLHF**) has become a central alignment recipe for language models and interactive agents: humans rank or compare outputs, a **reward model** is trained on those preferences, and a policy is then optimized against that learned signal.[1][4][6] Across the recent papers listed here, the field is moving from a practical training pipeline toward a deeper discussion of scalability, safety, and fundamental limits.[2][3][9]

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## Sources

1. [Training a helpful and harmless assistant with reinforcement learning from human feedback](https://arxiv.org/abs/2204.05862)
2. [Open problems and fundamental limitations of reinforcement learning from human feedback](https://arxiv.org/abs/2307.15217)
3. [A survey of reinforcement learning from human feedback](https://arxiv.org/abs/2312.14925)
4. [Rlhf deciphered: A critical analysis of reinforcement learning from human feedback for llms](https://dl.acm.org/doi/abs/10.1145/3743127)
5. [Rlaif: Scaling reinforcement learning from human feedback with ai feedback](https://openreview.net/forum?id=AAxIs3D2ZZ)
6. [Safe rlhf: Safe reinforcement learning from human feedback](https://proceedings.iclr.cc/paper_files/paper/2024/hash/dd1577afd396928ed64216f3f1fd5556-Abstract-Conference.html)
7. [A minimaximalist approach to reinforcement learning from human feedback](https://arxiv.org/abs/2401.04056)
8. [Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback](https://arxiv.org/abs/2309.00267)

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