@inproceedings{zhao-etal-2026-gift,
title = "{GIFT}: Reconciling Post-Training Objectives via Variational Finite-Temperature {G}ibbs Initialization",
author = "Zhao, Zhengyang and
Ma, Lu and
Jiang, Yizhen and
Ma, Xiaochen and
Meng, Zimo and
Shen, Chengyu and
Tang, Lexiang and
Sun, Haoze and
Pei, Peng and
Zhang, Wentao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1285/",
pages = "27215--27230",
ISBN = "979-8-89176-462-0",
abstract = "The prevailing post-training paradigm for Large Reasoning Models (LRMs){---}Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL){---}suffers from an intrinsic optimization mismatch: the rigid likelihood maximization in SFT induces distributional collapse, thereby exhausting the exploration space necessary for subsequent RL. Motivated by the Gibbs optimum of KL-regularized RL, we derive a token-level variational surrogate that makes the SFT target structurally compatible with the subsequent RL stage, and propose Gibbs Initialization with Finite Temperature (GIFT). Standard SFT emerges as a degenerate zero-temperature limit of this surrogate, while a finite temperature preserves structural diversity. Our experiments demonstrate that GIFT outperforms standard SFT and surpasses competitive baselines when utilized for RL initialization. Our code is available at \url{https://github.com/zzy1127/GIFT}."
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<abstract>The prevailing post-training paradigm for Large Reasoning Models (LRMs)—Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL)—suffers from an intrinsic optimization mismatch: the rigid likelihood maximization in SFT induces distributional collapse, thereby exhausting the exploration space necessary for subsequent RL. Motivated by the Gibbs optimum of KL-regularized RL, we derive a token-level variational surrogate that makes the SFT target structurally compatible with the subsequent RL stage, and propose Gibbs Initialization with Finite Temperature (GIFT). Standard SFT emerges as a degenerate zero-temperature limit of this surrogate, while a finite temperature preserves structural diversity. Our experiments demonstrate that GIFT outperforms standard SFT and surpasses competitive baselines when utilized for RL initialization. Our code is available at https://github.com/zzy1127/GIFT.</abstract>
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%0 Conference Proceedings
%T GIFT: Reconciling Post-Training Objectives via Variational Finite-Temperature Gibbs Initialization
%A Zhao, Zhengyang
%A Ma, Lu
%A Jiang, Yizhen
%A Ma, Xiaochen
%A Meng, Zimo
%A Shen, Chengyu
%A Tang, Lexiang
%A Sun, Haoze
%A Pei, Peng
%A Zhang, Wentao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhao-etal-2026-gift
%X The prevailing post-training paradigm for Large Reasoning Models (LRMs)—Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL)—suffers from an intrinsic optimization mismatch: the rigid likelihood maximization in SFT induces distributional collapse, thereby exhausting the exploration space necessary for subsequent RL. Motivated by the Gibbs optimum of KL-regularized RL, we derive a token-level variational surrogate that makes the SFT target structurally compatible with the subsequent RL stage, and propose Gibbs Initialization with Finite Temperature (GIFT). Standard SFT emerges as a degenerate zero-temperature limit of this surrogate, while a finite temperature preserves structural diversity. Our experiments demonstrate that GIFT outperforms standard SFT and surpasses competitive baselines when utilized for RL initialization. Our code is available at https://github.com/zzy1127/GIFT.
%U https://aclanthology.org/2026.findings-emnlp.1285/
%P 27215-27230
Markdown (Informal)
[GIFT: Reconciling Post-Training Objectives via Variational Finite-Temperature Gibbs Initialization](https://aclanthology.org/2026.findings-emnlp.1285/) (Zhao et al., Findings 2026)
ACL
- Zhengyang Zhao, Lu Ma, Yizhen Jiang, Xiaochen Ma, Zimo Meng, Chengyu Shen, Lexiang Tang, Haoze Sun, Peng Pei, and Wentao Zhang. 2026. GIFT: Reconciling Post-Training Objectives via Variational Finite-Temperature Gibbs Initialization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 27215–27230, Budapest, Hungary. Association for Computational Linguistics.