TTCS: Test-Time Curriculum Synthesis for Self-Evolving
Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high-quality pseudo-labels, and the limited size of test sets makes continuous online updates prone to instability. To address these limitations, we propose TTCS, a co-evolving test-time training framework. Specifically, TTCS initializes two policies from the same pretrained model: a question synthesizer and a reasoning solver. These policies evolve through iterative optimization: the synthesizer generates progressively challenging question variants conditioned on the test questions, creating a structured curriculum tailored to the solver's current capability, while the solver updates itself using self-consistency rewards computed from multiple sampled responses on both original test and synthetic questions. Crucially, the solver's feedback guides the synthesizer to generate questions aligned with the model's current capability, and the generated question variants in turn stabilize the solver's test-time training. Experiments show that TTCS consistently strengthens the reasoning ability on challenging mathematical benchmarks and transfers to general-domain tasks across different LLM backbones, highlighting a scalable path towards dynamically constructing test-time curricula for self-evolving. Our code and implementation details are available at https://github.com/XMUDeepLIT/TTCS.
6980103b6676f9332270654a
Chengyi Yang
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2026-02-02T16:56:38.598Z
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/avatars/1a2785d7a250c4988b1c1c5cc78e53fc.svg
ChengyiYang
ChengyiYang
user
ChengyiYang
6980103b6676f9332270654b
Zhishang Xiang
6980103b6676f9332270654c
Yunbo Tang
6980103b6676f9332270654d
Zongpei Teng
6980103b6676f9332270654e
Chengsong Huang
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2026-02-04T12:33:32.534Z
62ea79dd01ed9b0e8f61ccd3
/avatars/70af83e0e267be39fcd5f23b85e2dafa.svg
Chengsong Huang
ChengsongHuang
user
ChengsongHuang
6980103b6676f9332270654f
Fei Long
6980103b6676f93322706550
Yuhan Liu
6980103b6676f93322706551
Jinsong Su
TTCS: Test-Time Curriculum Synthesis for Self-Evolving
Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high-quality pseudo-labels, and the limited size of test sets makes continuous online updates prone to instability. To address these limitations, we propose TTCS, a co-evolving test-time training framework. Specifically, TTCS initializes two policies from the same pretrained model: a question synthesizer and a reasoning solver. These policies evolve through iterative optimization: the synthesizer generates progressively challenging question variants conditioned on the test questions, creating a structured curriculum tailored to the solver's current capability, while the solver updates itself using self-consistency rewards computed from multiple sampled responses on both original test and synthetic questions. Crucially, the solver's feedback guides the synthesizer to generate questions aligned with the model's current capability, and the generated question variants in turn stabilize the solver's test-time training. Experiments show that TTCS consistently strengthens the reasoning ability on challenging mathematical benchmarks and transfers to general-domain tasks across different LLM backbones, highlighting a scalable path towards dynamically constructing test-time curricula for self-evolving. Our code and implementation details are available at https://github.com/XMUDeepLIT/TTCS.
TTCS is a co-evolving test-time training framework that enhances LLM reasoning abilities by iteratively generating challenging question variants and updating a reasoning solver through self-consistency rewards.
test-time training large language models pseudo-labels self-consistency rewards question synthesizer reasoning solver iterative optimization test-time curricula mathematical benchmarks general-domain tasks