RL models show 10% higher probe accuracy than SFT on math tasks
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
RL fine-tuning made mathematical reasoning representations more linearly separable than SFT, so answer correctness was easier to predict from hidden states. For production, this means RL-trained reasoning models may be more amenable to internal probes, confidence diagnostics, and layer-targeted interventions, while token budget variability should not be assumed to come from RL alone but from the broader training pipeline.
RL fine-tuning restructures LLMs to develop more linearly separable and hierarchical representations for mathematical problem-solving, achieving up to higher accuracy in predicting answer correctness, and this fundamentally changes how models process reasoning problems, potentially enabling more robust and reliable deployment of LLMs in production environments that require complex problem-solving.