@inproceedings{chen-etal-2026-studybench,
title = "{S}tudy{B}ench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?",
author = "Chen, Yinghao and
Chen, Zixi and
He, Bingxiang and
Qiao, Ziqing and
Gao, Huan-ang and
Xu, Yinuo and
Zuo, Yuxin and
Liu, Zeyuan and
Zhan, Yuhao and
Xiao, Chaojun",
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.2298/",
pages = "49272--49290",
ISBN = "979-8-89176-462-0",
abstract = "Humans need to study only a handful of well-written textbooks to master a discipline and attempt its hardest problems. We argue that an ideal self-evolution method should share the same property, that is \textbf{autonomously learning from raw training material for transferable problem-solving capability}. However, we still lack a direct measurement for it. We introduce \textbf{StudyBench}, a controlled physics benchmark that directly measures how efficiently a self-evolution method converts training material into capability. We organise the test set into an \textbf{Application Set}, consisting of difficult textbook problems and evaluating absorption ability, and a \textbf{Transfer Set}, consisting of olympiad-level problems and evaluating transfer ability. Benchmarking representative self-evolution methods across three base models, we find that improvements on the Application Set rarely translate to the harder Transfer Set. A guidance ablation exposes a \textbf{Guidance Gap}: even the strongest method closes only a small fraction of what the same material unlocks when supplied as in-context guidance. Besides, every method hits a \textbf{Compute Plateau}, saturating well before exhausting its compute budget. The remaining gap is therefore a method problem rather than a data or compute problem. By offering a clean and controlled benchmark, StudyBench turns self-evolution progress from an open-ended pursuit into a measurable target for future research. Our code is released at \url{https://github.com/thunlp/StudyBench}."
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<abstract>Humans need to study only a handful of well-written textbooks to master a discipline and attempt its hardest problems. We argue that an ideal self-evolution method should share the same property, that is autonomously learning from raw training material for transferable problem-solving capability. However, we still lack a direct measurement for it. We introduce StudyBench, a controlled physics benchmark that directly measures how efficiently a self-evolution method converts training material into capability. We organise the test set into an Application Set, consisting of difficult textbook problems and evaluating absorption ability, and a Transfer Set, consisting of olympiad-level problems and evaluating transfer ability. Benchmarking representative self-evolution methods across three base models, we find that improvements on the Application Set rarely translate to the harder Transfer Set. A guidance ablation exposes a Guidance Gap: even the strongest method closes only a small fraction of what the same material unlocks when supplied as in-context guidance. Besides, every method hits a Compute Plateau, saturating well before exhausting its compute budget. The remaining gap is therefore a method problem rather than a data or compute problem. By offering a clean and controlled benchmark, StudyBench turns self-evolution progress from an open-ended pursuit into a measurable target for future research. Our code is released at https://github.com/thunlp/StudyBench.</abstract>
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%0 Conference Proceedings
%T StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?
%A Chen, Yinghao
%A Chen, Zixi
%A He, Bingxiang
%A Qiao, Ziqing
%A Gao, Huan-ang
%A Xu, Yinuo
%A Zuo, Yuxin
%A Liu, Zeyuan
%A Zhan, Yuhao
%A Xiao, Chaojun
%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 chen-etal-2026-studybench
%X Humans need to study only a handful of well-written textbooks to master a discipline and attempt its hardest problems. We argue that an ideal self-evolution method should share the same property, that is autonomously learning from raw training material for transferable problem-solving capability. However, we still lack a direct measurement for it. We introduce StudyBench, a controlled physics benchmark that directly measures how efficiently a self-evolution method converts training material into capability. We organise the test set into an Application Set, consisting of difficult textbook problems and evaluating absorption ability, and a Transfer Set, consisting of olympiad-level problems and evaluating transfer ability. Benchmarking representative self-evolution methods across three base models, we find that improvements on the Application Set rarely translate to the harder Transfer Set. A guidance ablation exposes a Guidance Gap: even the strongest method closes only a small fraction of what the same material unlocks when supplied as in-context guidance. Besides, every method hits a Compute Plateau, saturating well before exhausting its compute budget. The remaining gap is therefore a method problem rather than a data or compute problem. By offering a clean and controlled benchmark, StudyBench turns self-evolution progress from an open-ended pursuit into a measurable target for future research. Our code is released at https://github.com/thunlp/StudyBench.
%U https://aclanthology.org/2026.findings-emnlp.2298/
%P 49272-49290
Markdown (Informal)
[StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?](https://aclanthology.org/2026.findings-emnlp.2298/) (Chen et al., Findings 2026)
ACL
- Yinghao Chen, Zixi Chen, Bingxiang He, Ziqing Qiao, Huan-ang Gao, Yinuo Xu, Yuxin Zuo, Zeyuan Liu, Yuhao Zhan, and Chaojun Xiao. 2026. StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 49272–49290, Budapest, Hungary. Association for Computational Linguistics.