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Source note2 min read

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF

Source

arXiv

arxiv.org/abs/2607.07693v1

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Plain-Language Brief

The paper proposes two methods for improving the efficiency of Reinforcement Learning from Human Feedback (RLHF) when applied to diffusion models, which are often inefficient due to high feedback requirements. The first method involves a per-timestep weighting scheme during optimization that emphasizes informative timesteps, and the second introduces a replay mechanism prioritizing informative trajectories for better utilization of past samples. These strategies lead to significant improvements in sample efficiency when compared to existing diffusion RLHF baselines.

What the Document Says

  • The paper discusses Reinforcement Learning from Human Feedback (RLHF) and its application to diffusion models.
  • Diffusion models are often feedback-inefficient, requiring many human or reward model evaluations.
  • The proposed methods aim to improve the feedback efficiency of diffusion RLHF while preserving generalization to unseen prompts.
  • A key observation is that reward information in diffusion trajectories is unevenly distributed.
  • The first method introduces a per-timestep weighting scheme during optimization, focusing on informative timesteps for effective gradient updates.
  • The second method uses a replay mechanism to prioritize informative trajectories and reuse past samples instead of querying new rewards.
  • The strategies result in significant improvements in sample efficiency compared to existing diffusion RLHF baselines.

Key Concepts Mentioned

  • Reinforcement Learning from Human Feedback (RLHF)
  • diffusion models
  • feedback efficiency
  • per-timestep weighting
  • replay mechanism
  • sample efficiency

Claims Made by the Document

  • Existing diffusion RLHF approaches are feedback-inefficient.
  • Proposed strategies significantly improve the feedback efficiency of diffusion RLHF.
  • The proposed methods preserve generalization to unseen prompts.

Evidence Used

  • Improved sample efficiency compared to existing baselines.

Methods / Approach

  • Per-timestep weighting during optimization
  • Replay mechanism

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, applying RLHF to diffusion models remains highly feedback inefficient, as existing approaches typically require large amounts of human or reward model evaluations. This limitation reduces the practicality of diffusion RLHF in realworld settings where feedback is the primary bottleneck. In this paper, we propose two complementary strategies that substantially improve the feedback efficiency of diffusion RLHF while preserving generalization to unseen prompts. Our key observation is that reward information in diffusion trajectories is unevenly distributed: not all denoising timesteps or trajectories contribute equally to learning from a reward signal. By emphasizing informative timesteps and trajectories during optimization, we obtain more effective gradient updates. First, we introduce a per-timestep weighting scheme that reweights denoising steps during policy optimization. We theoretically connect this weighting to the optimal convergence properties of proximal policy optimization (PPO) and approximate the resulting weighting trend empirically. Second, we introduce a replay mechanism that prioritizes informative trajectories, enabling the model to reuse past samples instead of repeatedly querying new rewards. Together, these strategies significantly improve the feedback efficiency of diffusion RLHF. Under identical hyperparameter settings, our approach achieves up to a 6$\times$ improvement in sample efficiency compared to widely used diffusion RLHF baselines.