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  "title": "RLHF: Research Digest",
  "description": "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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      "label": "Papers",
      "value": "10"
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      "value": "reinforcement learning from human feedback"
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  "data": {
    "topic": "reinforcement learning from human feedback",
    "papers": [
      {
        "title": "Training a helpful and harmless assistant with reinforcement learning from human feedback",
        "url": "https://arxiv.org/abs/2204.05862",
        "year": ""
      },
      {
        "title": "Open problems and fundamental limitations of reinforcement learning from human feedback",
        "url": "https://arxiv.org/abs/2307.15217",
        "year": ""
      },
      {
        "title": "A survey of reinforcement learning from human feedback",
        "url": "https://arxiv.org/abs/2312.14925",
        "year": ""
      },
      {
        "title": "Rlhf deciphered: A critical analysis of reinforcement learning from human feedback for llms",
        "url": "https://dl.acm.org/doi/abs/10.1145/3743127",
        "year": ""
      },
      {
        "title": "Rlaif: Scaling reinforcement learning from human feedback with ai feedback",
        "url": "https://openreview.net/forum?id=AAxIs3D2ZZ",
        "year": ""
      },
      {
        "title": "Safe rlhf: Safe reinforcement learning from human feedback",
        "url": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/dd1577afd396928ed64216f3f1fd5556-Abstract-Conference.html",
        "year": ""
      },
      {
        "title": "A minimaximalist approach to reinforcement learning from human feedback",
        "url": "https://arxiv.org/abs/2401.04056",
        "year": ""
      },
      {
        "title": "Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback",
        "url": "https://arxiv.org/abs/2309.00267",
        "year": ""
      },
      {
        "title": "Reinforcement learning from human feedback",
        "url": "https://arxiv.org/abs/2504.12501",
        "year": ""
      },
      {
        "title": "Augmenting reinforcement learning with human feedback",
        "url": "https://www.ias.informatik.tu-darmstadt.de/uploads/Research/ICML2011/icml11il-knox.pdf",
        "year": ""
      }
    ]
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  "analysis_md": "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]\n\n## Core training pipeline and deployment\n\n*Training a helpful and harmless assistant with reinforcement learning from human feedback* presents RLHF as an iterative production method: preference modeling plus reinforcement learning, refreshed with new human feedback on a weekly cadence.[2] This work illustrates the now-standard loop of collecting comparisons, fitting a reward model, and updating the assistant to better satisfy human judgments.[2]\n\nThe survey *A survey of reinforcement learning from human feedback* consolidates this pipeline as the dominant RLHF pattern, while noting that direct policy optimization from human feedback is also possible.[3] In the broader technical framing summarized by *Reinforcement Learning from Human Feedback* and related overviews, the key idea is to replace a hand-designed reward with human-provided signals such as preferences, ratings, corrections, or demonstrations.[1][4][6]\n\n## Scaling, safety, and alternatives\n\n*RLAIF: Scaling reinforcement learning from human feedback with AI feedback* and *RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback* examine whether AI-generated feedback can reduce annotation cost.[5] Their reported takeaway is important: reward models trained on human feedback outperform those trained on AI feedback when evaluated against held-out human preferences.[5]\n\n*Safe RLHF: Safe Reinforcement Learning from Human Feedback* extends the framework toward explicit safety objectives, treating “harmlessness” as a first-class training concern rather than an emergent side effect.[6] In parallel, *A minimaximalist approach to reinforcement learning from human feedback* proposes Self-Play Preference Optimization (SPO), emphasizing a more minimalist alternative to the standard RLHF stack.[7]\n\n## Conceptual critiques and limits\n\n*Open problems and fundamental limitations of reinforcement learning from human feedback* frames RLHF as a sequence of hard subproblems: collecting feedback, training the reward model, and training the policy.[2][3] *RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs* and *Helpful, harmless, honest? Sociotechnical limits of AI alignment and RLHF* push further, arguing that RLHF’s apparent success can mask unresolved issues in how human preferences are represented, aggregated, and aligned with real-world values.[3][9]\n\n## Open problems\n\n- **Feedback quality and coverage:** how to collect human judgments that are consistent, representative, and cheap enough to scale.[2][3]\n- **Reward model reliability:** how to prevent reward models from overfitting narrow preferences or misgeneralizing outside the training distribution.[2][3][9]\n- **Policy optimization stability:** how to train policies safely and efficiently from imperfect preference signals.[2][3]\n- **Safety specification:** how to encode *helpful*, *harmless*, and *honest* objectives without collapsing them into simplistic proxies.[3][6][9]\n- **Scalable supervision:** whether AI feedback can complement human feedback without degrading alignment quality.[5]\n- **Alternative objectives:** whether minimalist methods such as SPO can match or surpass standard RLHF pipelines in practice.[7]\n\n1. [Training a helpful and harmless assistant with reinforcement learning from human feedback](https://arxiv.org/abs/2204.05862)\n2. [Open problems and fundamental limitations of reinforcement learning from human feedback](https://arxiv.org/abs/2307.15217)\n3. [A survey of reinforcement learning from human feedback](https://arxiv.org/abs/2312.14925)\n4. [Rlhf deciphered: A critical analysis of reinforcement learning from human feedback for llms](https://dl.acm.org/doi/abs/10.1145/3743127)\n5. [Rlaif: Scaling reinforcement learning from human feedback with ai feedback](https://openreview.net/forum?id=AAxIs3D2ZZ)\n6. [Safe rlhf: Safe reinforcement learning from human feedback](https://proceedings.iclr.cc/paper_files/paper/2024/hash/dd1577afd396928ed64216f3f1fd5556-Abstract-Conference.html)\n7. [A minimaximalist approach to reinforcement learning from human feedback](https://arxiv.org/abs/2401.04056)\n8. [Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback](https://arxiv.org/abs/2309.00267)\n9. [Reinforcement learning from human feedback](https://arxiv.org/abs/2504.12501)\n10. [Augmenting reinforcement learning with human feedback](https://www.ias.informatik.tu-darmstadt.de/uploads/Research/ICML2011/icml11il-knox.pdf)",
  "sources": [
    {
      "title": "Training a helpful and harmless assistant with reinforcement learning from human feedback",
      "url": "https://arxiv.org/abs/2204.05862"
    },
    {
      "title": "Open problems and fundamental limitations of reinforcement learning from human feedback",
      "url": "https://arxiv.org/abs/2307.15217"
    },
    {
      "title": "A survey of reinforcement learning from human feedback",
      "url": "https://arxiv.org/abs/2312.14925"
    },
    {
      "title": "Rlhf deciphered: A critical analysis of reinforcement learning from human feedback for llms",
      "url": "https://dl.acm.org/doi/abs/10.1145/3743127"
    },
    {
      "title": "Rlaif: Scaling reinforcement learning from human feedback with ai feedback",
      "url": "https://openreview.net/forum?id=AAxIs3D2ZZ"
    },
    {
      "title": "Safe rlhf: Safe reinforcement learning from human feedback",
      "url": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/dd1577afd396928ed64216f3f1fd5556-Abstract-Conference.html"
    },
    {
      "title": "A minimaximalist approach to reinforcement learning from human feedback",
      "url": "https://arxiv.org/abs/2401.04056"
    },
    {
      "title": "Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback",
      "url": "https://arxiv.org/abs/2309.00267"
    }
  ],
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