From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty | ScienceToStartup | ScienceToStartup

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References (25)

[1]

Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs

2025Xumeng Wen, Zihan Liu et al.

[2]

Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

2025Jingcheng Hu, Yinmin Zhang et al.

[3]

Rewarding Doubt: A Reinforcement Learning Approach to Calibrated Confidence Expression of Large Language Models

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"Develop a pipeline to efficiently infer calibrated uncertainty estimates in LLMs for high-stakes domains."

LLM Calibration•Score: 3•View PDF ↗

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From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty

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References (25)

Founder's Pitch

"Develop a pipeline to efficiently infer calibrated uncertainty estimates in LLMs for high-stakes domains."

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From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty

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References (25)

Founder's Pitch

"Develop a pipeline to efficiently infer calibrated uncertainty estimates in LLMs for high-stakes domains."

Commercial Viability Breakdown

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