New post-training example: SFT on agent traces with TRL
→ A large model works as a coding agent in OpenCode, run through OpenEnv → The tasks it solves become the training data → A 2B model goes from 5.5% to 34.4% on held-out tasks
About 500 traces get most of the gain. Five times more add only 3 to 4 points, so SFT gets the agent working and RL has to do the rest