scalable-rl

ARES proposes a framework for automatically constructing rubric-based RL data from pretraining documents, generating question-answer pairs and weighted rubrics to enable instance-level reward supervision for open-ended LLM responses, outperforming existing methods on multi-dimensional open-ended tasks.