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Self-evolving rubric rewards can dramatically enhance audio reasoning in models, outperforming traditional methods by adapting to the model's evolving capabilities.
Distilling from weaker models can enable a student to outperform its stronger counterparts, challenging the conventional wisdom of model hierarchy in AI training.
State-of-the-art LLMs fail to capture nuanced user preferences, lagging behind simple baselines in predicting choices in interactive narratives.
Forget human-readable models: Agentic-imodels evolves ML models that are optimized for LLM interpretability, boosting agentic data science performance by up to 73%.
