Caveat: This is a preliminary model trained with short-context data (768 token budget). AIME problems require long reasoning chains (16K+ tokens). A full-length version (R18) is in progress. Results below reflect the short-context constraint, not the method's ceiling.

Results (AIME 2025, 30 problems, 10 runs, temperature=0.6)

Model	Mean ± SE
CODI (paper method)	8.7% ± 1.3%
QThink (ours)	8.3% ± 1.1%
SFT	7.3% ± 1.0%
Base Qwen3-1.7B	6.3% ± 1.3%

All methods trained with matched 768-token budget for fair comparison. Differences are within standard error on this small test set.

For reference, the official Qwen3-1.7B in thinking mode achieves 65.6% on AIME 2025 using 32K+ generation tokens (Qwen3 Technical Report, arXiv 2505.09388).

Cross-Benchmark Results (QThink best)

Benchmark	QThink	SFT	Base	CODI
GSM8k	83.2%	80.7%	77.3%	78.2%
MATH-500	43.6%	38.2%	33.6%	31.2%
Tooluse	48.5%	45.6%	47.1%	42.6%
AIME 2025	8.3%	7.3%	6.3%	8.7%

Training Config

Base model: Qwen/Qwen3-1.7B with LoRA (rank=32, alpha=16)
Mode: uniform multi-rollout per-step distillation (gamma=2.0, K=6 latent steps)
Training data: DAPO-Math-17K English (14.1K problems, 16 rollouts each)
Student budget: max_prompt=512, max_answer=256 (768 total)
Teacher states: precomputed from full rollouts (up to 4096 tokens)

LakshyAAAgrawal/QThink-Qwen3-1.7B-AIME2025

QThink-Qwen3-1.7B-AIME2025

Results (AIME 2025, 30 problems, 10 runs, temperature=0.6)

Cross-Benchmark Results (QThink best)

Training Config

LakshyAAAgrawal/
QThink-Qwen3-1.7B-AIME2025