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Reasoning fine-tuning reorganizes latent dynamics, leading to significant performance improvements that traditional methods fail to achieve.
STRIDE reveals that training data influences can be efficiently traced in LLMs using sparse recovery, achieving attribution 13 times faster than traditional methods.
LLM activation spaces aren't linear, and exploiting their true geometry with "Curveball steering" unlocks more effective control than standard linear interventions.
