Rethinking the Role of Temperature in Large Language Model Distillation

2026-05-29 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new analysis re-evaluates the role of temperature (τ) in large language model (LLM) distillation, challenging the common preference for Reverse Kullback-Leibler (RKL) divergence over Forward KL (FKL). This work demonstrates that temperature significantly alters the comparison between FKL and RKL, revealing an asymmetric effect where FKL is substantially enriched by non-dominant token signals at higher temperatures, while RKL gradients are primarily rescaled. This asymmetry leads to FKL consistently surpassing RKL on instruction-following benchmarks when higher temperatures are applied, overturning the standard empirical conclusion that RKL outperforms FKL at τ=1. Furthermore, the study finds that temperature scaling enhances a broader range of distillation objectives, allowing simple KL-based methods to achieve competitive performance against recent advanced LLM distillation approaches.

Key takeaway

For Machine Learning Engineers optimizing LLM distillation, you should reconsider the default preference for Reverse Kullback-Leibler divergence. When using Forward KL, applying higher temperatures can significantly improve performance on instruction-following tasks, potentially outperforming RKL. Experiment with temperature scaling across your distillation objectives to enhance knowledge transfer and achieve competitive results with simpler KL-based methods.

Key insights

Temperature fundamentally changes FKL vs. RKL in LLM distillation, making FKL superior at higher temperatures.

Principles

• Temperature asymmetrically benefits FKL over RKL.
• Higher temperatures improve FKL performance in distillation.
• Temperature enhances various KL-based distillation objectives.

Method

The article analyzes the effect of temperature (τ) on FKL and RKL divergence in LLM distillation, comparing their performance across instruction-following benchmarks.

In practice

• Apply higher temperatures for FKL-based distillation.
• Re-evaluate RKL preference in LLM distillation.
• Explore temperature scaling for diverse KL objectives.

Topics

• LLM Distillation
• Temperature Scaling
• Forward KL Divergence
• Reverse KL Divergence
• Knowledge Transfer
• Instruction Following

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.