TTCS: Test-Time Curriculum Synthesis for Self-Evolving

Chengyi Yang; Zhishang Xiang; Yunbo Tang; Zongpei Teng; Chengsong Huang; Fei Long; Yuhan Liu; Jinsong Su

cs.LGJan 30, 2026

TTCS: Test-Time Curriculum Synthesis for Self-Evolving

Authors: Chengyi Yang, Zhishang Xiang, Yunbo Tang, Zongpei Teng, Chengsong Huang, Fei Long, Yuhan Liu, Jinsong Su

Organizations: Xiamen University · Washington University in St. Louis · Renmin University of China

Abstract

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.

Figures & tables

Figure 1: Comparison of TTRL and our TTCS: (a) When applied to difficult test questions such as AIME24, TTRL suffers from noisy rewards caused by incorrect majority voting consensus. (b) TTCS synthesizes tractable variants to ensure valid pseudo-labels, providing reliable supervision for stable self-evolution.

Figure 2: Overview of TTCS, a co-evolving test-time training framework. ( a ) Synthesizer training : the synthesizer first rollout synthetic questions conditioned on test questions with the prompt template, and then is optimized via GRPO with the question quality reward. ( b ) Solver training : the solver performs online self-evolving on a mixture of test and synthetic questions with self-supervised rewards, updated via GRPO using the self-consistency reward.

Method	AIME 2024	AIME 2025	AMC23	MATH-500	Minerva	OlympiadBench	AVG
Qwen2.5-Math-1.5B
Pretrained model Qwen et al. (2025)	7.10	4.20	27.50	33.20	9.60	22.20	17.30
Self-Consistency Wang et al. (2023)	13.30	10.00	50.00	49.80	10.70	31.90	27.62
R-Zero Huang et al. (2025)	10.00	4.58	47.50	66.20	30.88	31.01	31.70
TTRL Zuo et al. (2025)	13.23	9.38	55.00	71.20	34.93	35.61	36.56
TTCS (Ours)	19.79	13.33	62.50	76.80	40.44	36.05	41.49

Table 1: Comparison of our TTCS framework against other baselines on mathematical benchmarks. Best results are highlighted in bold.

Figure 3: Generalization analysis. (a) General-domain generalization: Accuracy trends of TTCS and TTRL on general-domain reasoning benchmarks (MMLU-Pro, SuperGPQA) during test-time training on AIME25. The green dashed line indicates the R-Zero baseline. (b) Mathematical-domain generalization: Performance comparison on out-of-distribution (OOD) mathematical benchmarks.

Method	AIME24	AIME25	Minerva	AVG
Qwen2.5-Math-1.5B	13.23	9.38	34.93	19.18
+Strong Synthesizer	16.35	10.21	38.97	21.84
+ TTCS(Ours)	19.79	13.33	40.44	24.52

Table 2: Performance comparison on the Qwen2.5-Math-1.5B model investigating the impact of synthesizer capability. The Strong synthesizer variant employs a fixed Qwen2.5-14B-Instruct model to generate questions.

Figure 4: Data efficiency analysis of test-time training on AIME24.

Settings	AMC23		Olympiad		Minerva
	Score	$\Delta$	Score	$\Delta$	Score	$\Delta$
TTCS (Full)	62.50	–	36.05	–	40.44	–
w/o Synthesizer Training	55.00	$\downarrow$ 7.50	32.79	$\downarrow$ 3.26	37.50	$\downarrow$ 2.94
w/o Online Data Filter	60.00	$\downarrow$ 2.50	33.68	$\downarrow$ 2.37	38.60	$\downarrow$ 1.84
w/o Diversity Penalty	55.00	$\downarrow$ 7.50	35.31	$\downarrow$ 0.74	39.34	$\downarrow$ 1.10

Table 3: Ablation study of TTCS components on the Qwen2.5-Math-1.5B model. Performance drops (

\Delta

) are computed relative to the full TTCS setting.

Iteration	Case 1: Function Intersection	Case 2: Roots of Unity
Test Question (AIME24)	Define $f(x)=\|\|x\|-\tfrac{1}{2}\|$ and $g(x)=\|\|x\|-\tfrac{1}{4}\|$ . Find the number of intersections of the graphs of $y=4g(f(\sin(2\pi x)))$ and $x=4g(f(\cos(3\pi y)))$ .	Let $\omega\neq 1$ be a 13th root of unity. Find the remainder when $\prod_{k=0}^{12}(2-2\omega^{k}+\omega^{2k})$ is divided by 1000.
Iter 1	Find the number of integer solutions $(x,y)$ to the equation $4x^{2}-16y^{2}+14x-24y+9=0$ .	The polynomial $f(x)=x^{3}+x^{2}-x-1$ has three distinct roots, $p$ , $q$ , and $r$ . What is $p^{3}q^{3}+r^{3}q^{3}+p^{3}r^{3}$ ?
Iter 5	How many points $(x,y)$ lie at intersections between the graphs of $x=\|\sin(\pi y)\|$ and $y=\|\sin(\pi x)\|$ ?	Find the sum of all complex numbers $w$ such that the real part of $w$ is greater than 0 and $w^{3}+(4-\frac{58}{7}i)w^{2}-(6+13i)w+10=0$ .
Iter 10	Find the number of points where the graphs of $y=\|x+1\|+\|x-1\|$ and $y=\|2x-1\|+\|3x-2\|$ intersect.	Let $\omega\neq 1$ be a 13th root of unity. Compute the remainder when $\sum_{n=0}^{12}\omega^{n}$ is divided by 1000.
Iter 15	Find the sum of the $x$ -coordinates of the points where the graph of $f(x)=\sin^{-1}\left(\frac{3x-x^{3}}{1+2x^{2}}\right)$ intersects the line $y=\frac{\pi}{4}$ , for $-\sqrt{2}\leq x\leq\sqrt{2}$ .	Let $N$ be the number of ordered triples $(a,b,c)$ of positive integers such that $a^{2}+b^{2}+c^{2}=2025$ . Find $N$ modulo 1000.

Table 4: Evolution of synthesized questions across training iterations. Two representative cases from AIME24 are shown, demonstrating how our self-evolving framework progressively improves question synthesis quality.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Config	Synthesizer Training	Solver Training
Batch Size	32	64
Learning Rate	1e-6	1e-6
Weight Decay	0.01	0.01
KL Coefficient	0.01	0.01
Max Steps	5	15
Rollout Group Size	4	8

Appendix

Table 5: Detailed training configurations and hyper-parameters for the synthesizer and solver agents.

Figure 5: The general-domain performance comparison of TTCS and the other baselines when TTCS and TTRL trained on AIME24 dataset.

Figure 6: The general-domain performance comparison of TTCS and the other baselines when TTCS and TTRL trained on AIME25 dataset.

Figure 7: The out-of-distribution performance comparison of TTCS and other baselines on mathematical benchmarks.

Test-Time Training

Test-time training adapts model parameters or memory during deployment using each input or stream to improve performance under changing tasks and data.

See all 99 papers

TTSR: Test-Time Self-Evolving via Reflection
Feb 6, 2026Haoyang He, Zihua Rong, Yunjia Zhao +3LLM Self-Refinement Test-Time Optimization
Beijing University of Posts and Telecommunications · Southwestern University of Finance and Economics · China Unicom Online Information Technology Co., Ltd.
Query-Conditioned Test-Time Self-Training for Large Language Models
May 13, 2026Chaehee Song, Minseok Seo, Yeeun Seong +2Test-Time Adaptation Test-Time Optimization
School of Electrical Engineering, KAIST · 2Graduate School of Green Growth and Sustainability, KAIST
DiSCTT: Consensus-Guided Self-Curriculum for Efficient Test-Time Adaptation in Reasoning
Mar 5, 2026Mohammad Mahdi Moradi, Sudhir MudurTest-Time Adaptation Self-Training for Language Models
Department of Compute Science, Concordia University, Montreal, Canada.

Feb 6, 2026J/K move · Enter open · S save

TTSR: Test-Time Self-Evolving via Reflection

Haoyang He, Zihua Rong, Yunjia Zhao, Lan Yang+2

Beijing University of Posts and Telecommunications · Southwestern University of Finance and Economics · China Unicom Online Information Technology Co., Ltd.

LLM Self-Refinement Test-Time Optimization Test-Time Training Teacher-Student Learning Language Model Self-Improvement

Open paper AskPDF arXiv

Test-time training (TTT) adapts large language models (LLMs) during inference using only unlabeled test inputs. Existing methods, however, face two major bottlenecks on hard reasoning tasks: (1) lack of learnable samples, as self-generated pseudo-labels on difficult questions are often noisy and yield unstable rewards; and (2) inefficient exploration, as performance gains depend on repeatedly sampling many rollouts without explicit diagnosis of why previous attempts fail. We propose TTSR (Test-Time Self-Reflection), a self-evolving framework based on a reflect-then-synthesize paradigm. A single pretrained model alternates between a Student role and a Teacher role: the Student solves test questions and updates, while the Teacher analyzes failed trajectories and synthesizes targeted variant questions closer to the Student's capability frontier. TTSR further maintains a cross-iteration weakness memory and compiles persistent weaknesses into a lightweight strategy note prepended to subsequent Student inputs, so diagnostic knowledge can guide exploration and gradually fade as weaknesses are resolved. Experiments on challenging mathematical reasoning benchmarks show consistent test-time improvements, strong cross-backbone generalization, and transfer to general-domain reasoning tasks.

Abstract

Test-Time Training

Explore similar work

TTSR: Test-Time Self-Evolving via Reflection

Query-Conditioned Test-Time Self-Training for Large Language Models

DiSCTT: Consensus-Guided Self-Curriculum for Efficient Test-Time Adaptation in Reasoning