> Canonical page: https://posttrainllm.com/inspiration/qorl

A note of thanks · Evaluation

# QORL

QORL helped us ask a harder database question: does offline search still win after a native planner baseline, held-out parameters, routing overhead, and tuning cost? The experiment remains unstarted.

01 · The idea

## What stayed with us

Parameter-aware query optimization needs equal search budgets, fresh held-out measurement, SQL-equivalence checks, and a native-planner baseline before an offline plan search can claim value.

02 · The local translation

## What we did with it

The owner-proposed local experiment isolates offline search and a parameter-to-plan dispatcher while keeping language-model training outside the scope.

03 · The boundary

## Where the comparison stops

Selection bias, cache state, parameter skew, routing overhead, and tuning cost can erase an apparent speedup.

Source trail

## Follow the work

- Original project [QORL project ↗](https://rohanbansal.com/qorl)
- Our evidence · learning queued [QORL ↗](https://posttrainllm.com/studies/qorl)

Independent appreciation. The named projects have not endorsed or affiliated with PostTrainLLM.