browser-use / rl-env-use

# rl-env-use
Introducing **rl-env-use**: RL environments for anyone and any domain.
Describe a skill in one line. Get a verified environment that frontier models do not saturate, and a hillclimbed prompt
for the model you train. Built on Anthropic's Automating eval design and hillclimbing
workflow, with one change: the tasks are real cases that Browser Use agents mine
from the web.
| Missions | Claude Code | Browser Use |
|----------|-------------|-------------|
| 8 x clinical trial results | 0 of 86 facts verified | **17 verified**, 4 complete cases |
| 6 x clinical trial results, same model both sides | 0 of 43 | **3 verified**, 2 complete cases |
| 8 x human genetic variants (UniProt, gnomAD, GTEx) | 6 of 82 | **40 verified** |
Get a Browser Use API key at Browser Use Cloud.
## What it does
- **Mines real cases**: Browser Use agents find the materials (a trial protocol, a 10-Q) and the outcome published later
- **Writes hard tasks**: gold answers computed by code, anything a frontier panel solves gets rewritten harder
- **Rejects bad tasks**: leaked answers, memorized outcomes, ambiguous prompts
- **Hillclimbs** a model's system prompt with a train/test split, a noise floor and paired confidence intervals
- **Exports** the dataset for an RL trainer, or serves it in code with `Env`
## Setup prompt
For Claude Code / Codex / OpenCode:
 [code] 
Then ask:
> build me an RL environment for predicting whether a clinical trial met its primary endpoint, from its ClinicalTrials.gov registration
For humans:
 [code] 
## Results
The shipped example, `envs/trial-forecast`: 35 forecasts from 29 ClinicalTrials.gov trials whose
results were posted since July 2026, after the models' training data. Built for $72, $17 of it Browser Use.
A climbed Qwen 3.6 35B-A3B goes from 0.53 to 0.78 on tasks it never trained on (+0.25, 95% CI +0.07 to +0.45), level
with Opus 5.5. Always answering 50% scores 0.75, so most of the gain is calibration: the model stopped answering 0 or 1.
The climb kept 1 of 12 proposed edits (base-rate priors per question type); every round is in
`climb-qwen-35b/`.
## Manual install
Python 3.11+, optionally Node.js 18+ for the HTML reports.
 [code] 
See `SKILL.md` for the full workflow and where every file is saved.
## License
MIT, see LICENSE.