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OpenAI finds evolution strategies rival reinforcement learning for AI training

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

OpenAI researchers have found that evolution strategies (ES), a decades-old optimization technique, can rival the performance of modern reinforcement learning (RL) methods on benchmarks like Atari and MuJoCo. ES offers advantages such as simpler implementation without backpropagation, easier scalability in distributed settings, and better handling of sparse rewards. This approach trains agents significantly faster than traditional RL, with one experiment reducing training time for a humanoid walker from 10 hours to 10 minutes. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

RANK_REASON This is a research paper from OpenAI detailing a novel application of a known optimization technique to AI benchmarks.

Read on Lil'Log (Lilian Weng) →

OpenAI finds evolution strategies rival reinforcement learning for AI training

COVERAGE [2]

  1. OpenAI News TIER_1 ·

    Evolution strategies as a scalable alternative to reinforcement learning

    We’ve discovered that evolution strategies (ES), an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.

  2. Lil'Log (Lilian Weng) TIER_1 ·

    Evolution Strategies

    <!-- Gradient descent is not the only option when learning optimal model parameters. Evolution Strategies (ES) works out well in the cases where we don't know the precise analytic form of an objective function or cannot compute the gradients directly. This post dives into several…

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