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Earlier quoted context omitted.
i read around launch that anthropic will fallback to opus if fable is used for frontier LLM development. did you run into anything like that?
Strangely, I did not. I was expecting it and looking out for it the whole time. At least I never say a warning!
No idea why you got downvoted into oblivion with the context post. Cool idea!
Because HN detects AI comments and automatically makes them dead. I vouched for it as it's important for this post as context.
Earlier quoted context omitted.
Because HN detects AI comments and automatically makes them dead. I vouched for it as it's important for this post as context.
Trying to understand, So you downvote the post because of AI comments in HN ?
How do you prevent the agent from reward-hacking the hidden eval? e.g. writing training data that effectively leaks the eval distribution rather than teaching a general skill?
No signs of it in the run I did, but for any future larger scale runs, it would be wise to dedicate some focus to prevent this.
I RL-trained an agent whose job is to write RL training jobs for smaller models, and open-sourced the whole thing. The setup is two nested RL loops: - Outer loop: the trainer agent (Qwen3.6-35B-A3B, LoRA) is handed a task spec ("teach a small model to do X"). It works in a sandboxed workspace with file tools and writes a complete prime-rl training job: a verifiers environment + rubric, a dataset, and a hyperparameter…