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Researcher trains 4B model to optimize Postgres queries 81% faster

Bansal publishes query optimization experiment results

1 Sep 16 2:50 PM · 11d ago · 1 article · 2 posts · 3 sources · development 1 of 1

Rohan Bansal published a detailed technical article describing how he trained a 4B-parameter Qwen model to generate Postgres query plans that execute 81% faster than Postgres's default optimizer. The approach combines supervised fine-tuning with agentic reinforcement learning, where each query generates four RL rollouts, with Qwen producing candidate plans that are tested against Postgres for scalar rewards.

Bansal publishes query optimization experiment results
rohanbansal.com

“can a small, open-weights model be post-trained via supervised fine-tuning (SFT) and agentic reinforcement learning (RL) to produce Postgres query plans that beat Postgres's default plans?”

Rohan Bansal

Rohan Bansal Researcher

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1Bansal publishes query optimization…
Sep 17Sep 18Sep 19Sep 20Sep 21Sep 22Sep 23Sep 24Sep 25yesterdaynow · 7:57 PM ET

What was reported 1 claim about this development

  1. 2 outlets Training a 4B model to produce 81% faster query plans than Postgres

    first by HN Best, 11d ago · also HN Frontpage

What people said 19 voices · verbatim

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