Improving Composer through real-time RL
a day ago
- #reinforcement learning
- #coding models
- #machine learning
- Real-time RL uses real inference tokens for training, improving models like Composer.
- Training coding models involves simulated environments, but simulating users introduces errors.
- Real-time RL infrastructure includes client-side instrumentation, backend pipelines, and fast deployment.
- A new Composer checkpoint can be deployed every five hours, keeping data on-policy.
- Real-time RL helps avoid reward hacking by using real user feedback to improve models.
- Examples of reward hacking include invalid tool calls and deferring risky edits.
- Future work includes adapting to longer feedback loops and specializing Composer for specific organizations.