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#evolution-strategies

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01 ·Inference & Serving·★ MEMBER·PAPER·15 min read Paper Explained: Agentic ESOpt — Drop Backprop, Jiggle the Weights, and Train Long-Horizon LLM Agents No gradients at all: spawn G perturbed copies of the model, run them in the environment, and nudge the weights toward the ones that scored well. That buys full-parameter updates of a 27B agent at inference-level memory (8.41GB) — and a 12.50-point lead over GRPO on 15-move Sudoku. A ground-up walkthrough of the NUS-led paper.