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TRL
1 post about TRL. Every summary links to the original.
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Training a coding model to paint watercolours with TRL and OpenEnv
The project reproduces an earlier idea: training a language model to write JavaScript that paints watercolours with p5.brush, using TRL and OpenEnv. Its open pipeline includes a hand-rated reference pool, an RL environment, training scripts, trained models, and Hugging Face Jobs, Spaces, Inference Providers, and Hub artifacts. The environment renders sketches in headless Chromium, restricts the model to ten library methods, rejects cheats through a gate, and combines gate, length, pairwise-judge, and HPSv3 rewards. Three reward mixes were trained: judge-led (0.60/0.30), hps-led (0.30/0.60), and hps-only (0.00/0.90); the first two ran to step 110, while validation stopped at step 60. The post reports that both pairwise-judge runs learned and identifies the hand-rated pool as the bottleneck.
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