arXiv cs.LG
· Papers
Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning
arXiv:2505.20161v2 Announce Type: replace Abstract: Effective generalization in language models depends critically on the diversity of their training data. Yet existing diversity metrics often fall short of this goal, relying on surface-level heuristics that are decoupled from model behavior. This motivates us to ask: