Qwen3-4B-Base (AI model): news timeline & CVEs

Semifactual Credit-Augmented Policy Optimization

SCAPO uses semifactual token stability to beat GRPO on AIME for two Qwen3 base models.

The paper studies how verifiable-reward reasoning models respond to semifactual prompt changes that preserve the problem and its answer. It finds token-level probability drift, and that suppressing high-drift candidates during decoding can improve accuracy without weight updates, exposing a weakness of GRPO's uniform token advantage. SCAPO uses normalized semifactual stability to reduce advantages for unstable tokens early in training without granting extra credit for stability alone. On Qwen3-4B-Base and Qwen3-1.7B-Base, it improves AIME 2024-2026 accuracy over GRPO by 5.63 and 4.17 percentage points and leads most math and all evaluated out-of-distribution benchmarks.

arXiv cs.AI / cs.LG / cs.CLSep 30, 17:59 UTC · 1d agoAI research

Self-Play Search Distillation for Large Language Model Reasoning

Self-play search distillation raises Qwen3-4B-Base's math benchmark mean from 24.1 to 36.6.

Self-Play Search Distillation generates synthetic reasoning data from MuZero-like networks trained by self-play on board games. At each state the search record names a preferred decision, plausible alternatives, opponent replies, and value estimates, which are converted into environment-grounded chains of thought. Although trained only on those game records, Qwen3-4B-Base raises its mean over six mathematics benchmarks from 24.1 to 36.6 and its held-out-game win rate from 15% to 45%. The authors present SPSD as an annotation-efficient alternative to low-quality synthetic data and costly human labels.

Hugging Face daily papersSep 24, 20:00 UTC · 7d agoAI research

Qwen3-4B-Base

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Semifactual Credit-Augmented Policy Optimization

Self-Play Search Distillation for Large Language Model Reasoning