QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
Weight-only quantization is important for compressing Large Language Models (LLMs). Inspired by the spirit of classical magnitude pruning, we study whether the magnitude of weight updates during reasoning-incentivized fine-tuning can provide valuable signals for quantizing Large Reasoning Models (LRMs). We hypothesize that the smallest and largest weight updates during fine-tuning are more important than those of intermediate magnitude, a phenomenon we term "protecting both ends". Upon hypothesis validation, we introduce QuantLRM, which stands for weight quantization of LRMs via fine-tuning signals. We fit simple restricted quadratic functions on weight updates to protect both ends. By multiplying the average quadratic values with the count of zero weight updates of channels, we compute channel importance that is more effective than using activation or second-order information. We run QuantLRM to quantize various fine-tuned models (including supervised, direct preference optimization, and reinforcement learning fine-tuning) over four reasoning benchmarks (AIME-120, FOLIO, temporal sequences, and GPQA-Diamond) and empirically find that QuantLRM delivers a consistent improvement for LRMs quantization, with an average improvement of 6.55% on a reinforcement learning fine-tuned model. Also supporting non-fine-tuned LRMs, QuantLRM gathers effective signals via pseudo-fine-tuning, which greatly enhances its applicability.
6987aa52beecc443208d23dc
Nan Zhang
claimed_verified
2026-02-09T08:30:46.297Z
63c6eb9e8bfd6a208eb2a593
/avatars/a55ea118af3d2bab2bc38c559abe3148.svg
Nan Zhang
nanzhang
user
nanzhang
6987aa52beecc443208d23dd
Eugene Kwek
6987aa52beecc443208d23de
Yusen Zhang
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2026-02-18T09:06:44.256Z
6226742d7551f131b4eba59b
/avatars/252f1fb7a82d34df4855241b5c8145ce.svg
Yusen Zhang
yusenzhang
user
yusenzhang
6987aa52beecc443208d23df
Muyu Pan
6987aa52beecc443208d23e0
Suhang Wang
6987aa52beecc443208d23e1
Prasenjit Mitra
6987aa52beecc443208d23e2
Rui Zhang
QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
Weight-only quantization is important for compressing Large Language Models (LLMs). Inspired by the spirit of classical magnitude pruning, we study whether the magnitude of weight updates during reasoning-incentivized fine-tuning can provide valuable signals for quantizing Large Reasoning Models (LRMs). We hypothesize that the smallest and largest weight updates during fine-tuning are more important than those of intermediate magnitude, a phenomenon we term "protecting both ends". Upon hypothesis validation, we introduce QuantLRM, which stands for weight quantization of LRMs via fine-tuning signals. We fit simple restricted quadratic functions on weight updates to protect both ends. By multiplying the average quadratic values with the count of zero weight updates of channels, we compute channel importance that is more effective than using activation or second-order information. We run QuantLRM to quantize various fine-tuned models (including supervised, direct preference optimization, and reinforcement learning fine-tuning) over four reasoning benchmarks (AIME-120, FOLIO, temporal sequences, and GPQA-Diamond) and empirically find that QuantLRM delivers a consistent improvement for LRMs quantization, with an average improvement of 6.55% on a reinforcement learning fine-tuned model. Also supporting non-fine-tuned LRMs, QuantLRM gathers effective signals via pseudo-fine-tuning, which greatly enhances its applicability.
QuantLRM uses weight update magnitude signals from fine-tuning to improve quantization of Large Reasoning Models, achieving better performance than traditional methods through channel importance estimation.
weight-only quantization Large Language Models Large Reasoning Models fine-tuning magnitude pruning weight updates restricted quadratic functions channel importance pseudo-fine-tuning