← Monday, September 28, 2026InferencePaper

FLEET uses generation history to reduce duplicate sampling and improve coding results

huggingface.co by Oleksii Streltsov, Oleksandra Vitko Editor score 8/10

FLEET adjusts logits using sparse, high-entropy generation trajectories; it reports 3× speedup at baseline accuracy and raises LiveCodeBench Pass@32 from 59.9% to 66.2% at the same budget. The method connects decoding decisions to prior samples rather than treating each draw independently.

What

- FLEET adds memory of prior generations to temperature sampling, representing generations as sparse trajectories through high-entropy states.

Method

- It uses trajectories to infer per-token utility scores that adjust logits; evaluations found the same accuracy as repeated sampling with a 3× speedup.

Why it matters

- For coding tasks, FLEET improved LiveCodeBench Pass@32 from 59.9% to 66.2% under the same budget, with minimal pipeline changes.

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