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arXiv Machine Learning By Zeyu Zheng, Clare Lyle, Khimya Khetarpal, Will Dabney

An Empirical Study of Reinforcement Learning Scaling Laws

Read the original on arXiv Machine Learning →

The paper presents an empirical investigation of scaling laws for reinforcement learning, focusing on an actor‑critic algorithm in the Starpilot and Craftax environments. It demonstrates that these scaling laws can accurately predict performance, optimal resource allocation, and hyperparameter settings for training jobs up to two orders of magnitude larger. The study also shows that Elo scores provide a more predictable performance metric than mean episode return, and uses the discovered laws to achieve a new state‑of‑the‑art result in Craftax while revealing a non‑monotonic scaling pattern linked to Ray interference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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