arXiv Machine Learning July 16, 2026 By Patrick Wilhelm, Odej Kao

Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback

Read the original on arXiv Machine Learning →

arXiv:2607. 13389v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained post-training resources are often summarized by a single total FLOP budget.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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