SFT vs RLVR: how fine-tuning paradigms shape LLM confidence calibration in high-stakes use
burkov · x · 2026-10-20
burkov highlights a study on LLM confidence calibration. As LLMs increasingly make high-stakes calls in content moderation, medical assistance, and financial services, they must express reliable confidence so downstream systems know when to trust a prediction and when to defer to a human or a larger model. Miscalibration means looking certain while wrong — an overconfident safety classifier can quietly pass harmful content.
The article compares two dominant fine-tuning paradigms:
- Supervised fine-tuning (SFT)
- RL with verifiable rewards (RLVR)
It measures both task accuracy and confidence trustworthiness, answering how common fine-tuning methods affect each.
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