@inproceedings{tang-etal-2026-next,
title = "Is Next-Chunk Reasoning {RL} Really Better than {SFT}? Revisiting Training Strategies under no-{C}o{T} Data",
author = "Tang, Yinhao and
Fang, Youqing and
Sun, Yanan and
Liu, Jiangning and
Wang, Ziyi and
Zhao, Xun and
Zhang, Weiming and
Liu, Bin and
Liu, Kuikun and
Zhang, Wenwei and
Chen, Kai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.921/",
pages = "21209--21228",
ISBN = "979-8-89176-459-0",
abstract = "Recent work proposes next-chunk reasoning RL for leveraging no-CoT data{--}corpora such as worked solutions and textbook derivations that contain reasoning-rich content but lack explicit chain-of-thought annotations. The method trains a model to generate implicit reasoning traces and rewards them by their ability to predict the next chunk of text. While promising, existing evaluations primarily compare against conventional SFT baselines, leaving open whether the gains come from the RL formulation itself or from more effectively exposing the model to no-CoT data. We address this question with a controlled study of next chunk reasoning RL and a simple but previously overlooked alternative: Mixed SFT, a single supervised fine-tuning stage that jointly trains on no-CoT and long-CoT data. Despite its simplicity, Mixed SFT achieves a clearly higher post-RLVR performance ceiling than next-chunk reasoning RL while requiring over 60 times less training compute. The advantage is consistent across in-domain mathematical reasoning and out-of-domain reasoning tasks. Moreover, we show that higher pre-RLVR accuracy does not necessarily translate into higher post-RLVR accuracy, highlighting the need to evaluate no-CoT training strategies in the context of the full post-training pipeline."
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<abstract>Recent work proposes next-chunk reasoning RL for leveraging no-CoT data–corpora such as worked solutions and textbook derivations that contain reasoning-rich content but lack explicit chain-of-thought annotations. The method trains a model to generate implicit reasoning traces and rewards them by their ability to predict the next chunk of text. While promising, existing evaluations primarily compare against conventional SFT baselines, leaving open whether the gains come from the RL formulation itself or from more effectively exposing the model to no-CoT data. We address this question with a controlled study of next chunk reasoning RL and a simple but previously overlooked alternative: Mixed SFT, a single supervised fine-tuning stage that jointly trains on no-CoT and long-CoT data. Despite its simplicity, Mixed SFT achieves a clearly higher post-RLVR performance ceiling than next-chunk reasoning RL while requiring over 60 times less training compute. The advantage is consistent across in-domain mathematical reasoning and out-of-domain reasoning tasks. Moreover, we show that higher pre-RLVR accuracy does not necessarily translate into higher post-RLVR accuracy, highlighting the need to evaluate no-CoT training strategies in the context of the full post-training pipeline.</abstract>
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%0 Conference Proceedings
%T Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data
%A Tang, Yinhao
%A Fang, Youqing
%A Sun, Yanan
%A Liu, Jiangning
%A Wang, Ziyi
%A Zhao, Xun
%A Zhang, Weiming
%A Liu, Bin
%A Liu, Kuikun
%A Zhang, Wenwei
%A Chen, Kai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tang-etal-2026-next
%X Recent work proposes next-chunk reasoning RL for leveraging no-CoT data–corpora such as worked solutions and textbook derivations that contain reasoning-rich content but lack explicit chain-of-thought annotations. The method trains a model to generate implicit reasoning traces and rewards them by their ability to predict the next chunk of text. While promising, existing evaluations primarily compare against conventional SFT baselines, leaving open whether the gains come from the RL formulation itself or from more effectively exposing the model to no-CoT data. We address this question with a controlled study of next chunk reasoning RL and a simple but previously overlooked alternative: Mixed SFT, a single supervised fine-tuning stage that jointly trains on no-CoT and long-CoT data. Despite its simplicity, Mixed SFT achieves a clearly higher post-RLVR performance ceiling than next-chunk reasoning RL while requiring over 60 times less training compute. The advantage is consistent across in-domain mathematical reasoning and out-of-domain reasoning tasks. Moreover, we show that higher pre-RLVR accuracy does not necessarily translate into higher post-RLVR accuracy, highlighting the need to evaluate no-CoT training strategies in the context of the full post-training pipeline.
%U https://aclanthology.org/2026.emnlp-main.921/
%P 21209-21228
Markdown (Informal)
[Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data](https://aclanthology.org/2026.emnlp-main.921/) (Tang et al., EMNLP 2026)
ACL
- Yinhao Tang, Youqing Fang, Yanan Sun, Jiangning Liu, Ziyi Wang, Xun Zhao, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, and Kai Chen. 2026. Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 21209–21228, Budapest, Hungary. Association for Computational Linguistics.