View: LLMs Should Be Trained at Sampling Temperature; RL Targets Canonical Temp
kalomaze · x · 2026-08-21
Addressing the debate on adjusting LLM temperature, the author supports the "platonic ideal":
- Training-Sampling Alignment: The conditional distribution used during training should match the distribution sampled from during downstream deployment.
- RLHF Strategy: On-policy reinforcement learning inherently aims to keep the model at the canonical temperature designated for deployment.
- Core Philosophy: Avoid introducing temperature discrepancies between training and inference.
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