adaptive-pipeline-orchestration
FLAGSHIPRL vs. rule-based data-pipeline orchestration under one shared utility — a rigorously hardened negative result.
A research study that reports what actually happened — the clever method didn't win — and lets anyone re-run every number.
- Python 85%
- TeX 13%
- Makefile 2%
Why this exists
Graduate research asking whether a self-learning agent can orchestrate data pipelines better than fixed rules. The honest headline is that on the original environment it can't — and the repo documents the skeptical-reviewer pass that made that claim defensible: disjoint seed pools, a reward-identity fix pinned by regression tests, and environment variants that show where learning does start to win.
Start reading here
I'd stress-test the environment before the agent: the benign default made a trivial policy optimal, and most of the hardening pass was earning back the right to make any claim at all.
Open to internships & contract work · full-time from 2027
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