TTCS: Test-Time Curriculum Synthesis for Self-Evolving

A self-evolving framework that enhances LLM mathematical reasoning through dynamic Synthesizer-Solver co-evolution at test time.

🔥 Updates

• [2026-3-3] Accepted by ICLR 2026 Lifelong Agent(LLA) workshop. See you in Rio De Janeiro, Brazil!

• [2026-3-1] Update codes to fix some bugs in previous version.

• [2026-2-2] TTCS got #3 Paper of the day in huggingface daily paper.

• [2026-1-30] We released our paper and code.

🏴 Overview

TTCS (Test-Time Curriculum Synthesis) is a co-evolving test-time training framework built on an iterative GRPO optimization loop. It consists of two agents: a Synthesizer policy $\pi_{\phi}$ and a Solver policy $\pi_{\theta}$, both initialized from the same pretrained model.

At each iteration:

• The Synthesizer generates curriculum variants for each test question, rewarded to preserve the reasoning structure while staying near the Solver's current capability frontier.
• The Solver performs online self-evolving on a mixture of synthetic and test questions, guided by self-consistency rewards.

The two agents co-evolve in a closed loop: the Solver's current performance provides a capability-aware training signal that shapes the Synthesizer's generation distribution, while the Synthesizer continuously supplies fresh, question-centered variants that stabilize the Solver's test-time training.

Key Features

• Test-Time Curriculum Synthesis: Generates structured, localized curriculum variants around each test question, allowing the Solver to learn from simpler, related variants.
• Capability-Aware Training: Question Quality Reward prioritizes synthetic questions at the Solver's capability frontier—neither too easy nor too hard.
• Co-Evolutionary Learning: A unique Synthesizer-Solver dynamic creates targeted, adaptive curriculum for mathematical reasoning.
• Self-Consistency Rewards: Uses majority voting mechanism to obtain pseudo-labels without external supervision.
• Model-Agnostic: Consistently improves performance across various backbone LLMs (1.5B to 7B parameters).

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📊 Main Results

The table below compares TTCS against other baselines on mathematical reasoning benchmarks. Best results are highlighted in bold.

Qwen2.5-Math-1.5B

Method	AIME 2024	AIME 2025	AMC23	MATH-500	Minerva	OlympiadBench	AVG
Pretrained Model	7.10	4.20	27.50	33.20	9.60	22.20	17.30
Self-Consistency	13.30	10.00	50.00	49.80	10.70	31.90	27.62
R-Zero	10.00	4.58	47.50	66.20	30.88	31.01	31.70
TTRL	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

Qwen2.5-Math-7B

Method	AIME 2024	AIME 2025	AMC23	MATH-500	Minerva	OlympiadBench	AVG
Pretrained Model	12.90	7.90	45.00	52.80	18.80	18.70	26.02
Self-Consistency	20.00	13.30	52.50	62.20	22.10	22.80	32.15
R-Zero	18.13	7.81	65.00	78.60	43.38	39.47	42.07
TTRL	35.52	14.06	67.50	83.40	49.26	40.80	48.42
TTCS (Ours)	37.19	19.90	75.00	84.60	53.31	45.25	52.54

Qwen3-4B-Base

Method	AIME 2024	AIME 2025	AMC23	MATH-500	Minerva	OlympiadBench	AVG
Pretrained Model	12.10	5.40	45.00	72.40	32.70	39.90	34.58
Self-Consistency	20.00	10.00	57.50	79.60	41.20	44.10	42.07
R-Zero	11.35	8.65	55.00	76.20	45.96	42.73	39.98
TTRL	16.67	17.81	57.50	80.40	45.96	43.18	43.59
TTCS (Ours)	25.00	19.58	60.00	81.80	52.21	44.66	47.21

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⚡️ Quickstart Guide

1. Environment Setup

# Clone the repository
git clone https://github.com/your-org/TTCS.git
cd TTCS

# Install dependencies
pip install -r requirements.txt

2. Configure Paths

Set up environment variables for your storage paths:

# Base directory for all data and results
export TTCS_BASE_DIR="/path/to/your/base"

# Model directory
export TTCS_MODEL_DIR="/path/to/your/models"

# Data directory
export TTCS_DATA_DIR="/path/to/your/data"

# Results directory
export TTCS_SAVED_RESULTS_DIR="/path/to/saved_results"

3. Run Training

The main entry point is run_with_gpus.sh, which handles GPU allocation and launches the training pipeline:

# Run with 4 GPUs
bash src/script/run_with_gpus.sh 4

# Run with 8 GPUs
bash src/script/run_with_gpus.sh 8

The script will:

1. Detect available GPUs and wait for sufficient resources
2. Automatically allocate GPUs for Synthesizer and Solver training
3. Launch the iterative co-evolutionary training loop
4. Run evaluation after training completes

4. Run Evaluation Only

To evaluate a trained model:

bash evaluation/eval.sh <variant_name> <eval_step> <num_iterations> <base_model_name> <dataset>

❓ FAQ

Q: What hardware is required?

A: Our experiments were conducted on servers with 4-8 GPUs. The minimum recommended setup is 4 GPUs (2 for Synthesizer, 2 for Solver/Reward). For larger models, 8 GPUs are recommended.

Q: How long does training take?

A: Training time depends on the model size and number of iterations. For Qwen2.5-Math-1.5B with 4 GPUs, each iteration takes approximately 4-5 hours.

Q: Can I use a different base model?

A: Yes! TTCS is model-agnostic. Modify the TTCS_BASE_MODEL_NAME and TTCS_BASE_MODEL_PATH environment variables to use your preferred model.

Q: Where are checkpoints saved?

A: Checkpoints are saved in ${TTCS_SAVED_RESULTS_DIR}/Solver_ttrl/ and ${TTCS_SAVED_RESULTS_DIR}/Synthesizer_ttrl/ for Solver and Synthesizer respectively.

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🙏 Acknowledgements

Our framework builds upon the excellent work of:

• R-Zero - Self-Evolving Reasoning LLM
• veRL - Volcano Engine Reinforcement Learning

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📝 Citation

If you find TTCS helpful for your research, please cite our paper:

@article{yang2026ttcs,
  title={TTCS: Test-Time Curriculum Synthesis for Self-Evolving},
  author={Yang, Chengyi and Xiang, Zhishang and Tang, Yunbo and Teng, Zongpei and Huang, Chengsong and Long, Fei and Liu, Yuhan and Su, Jinsong},
  journal={arXiv preprint arXiv:2601.22628},
  year={2026}
}

Links:

• 📄 arXiv: https://arxiv.org/abs/2601.22628
• 🤗 HuggingFace Papers: https://huggingface.co/papers/2601.22628

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.