DASD-4B-Thinking-2507-stage2 is the final model in a three-stage training pipeline built upon Qwen/Qwen3-4B-Thinking-2507. It combines Reinforcement Learning via GRPO with a two-stage Supervised Fine-Tuning (SFT) strategy inspired by the Distribution-Aligned Sequence Distillation (DASD) methodology introduced by Alibaba Cloud Apsara Lab, resulting in a compact 4B model with enhanced mathematical reasoning and long chain-of-thought capabilities.
🧬 Training Pipeline Overview
This model is the culmination of three sequential training stages:
Qwen/Qwen3-4B-Thinking-2507
│
▼ Stage 0: GRPO (RL on Math & Reasoning)
DASD-4B-Thinking-2507-GRPO-v2
│
▼ Stage 1: SFT with Low-Temperature (T=0.6) Distillation Data
DASD-4B-Thinking-2507-stage1
│
▼ Stage 2: SFT with Default-Temperature (T=1.0) Distillation Data
DASD-4B-Thinking-2507-stage2 ← (this model)
Starting from the base model Qwen/Qwen3-4B-Thinking-2507, Group Relative Policy Optimization (GRPO) was applied using a high-quality mathematical reasoning dataset distilled from DeepSeek-R1. This stage significantly improved the model's:
💡 Why Low-Temperature Distillation for Small Models?
Low-temperature sampling from the teacher model (gpt-oss-120b) produces sharper, more deterministic output distributions, which are significantly easier for small student models to imitate and internalize. This "cold-start" strategy:
Reduces distributional mismatch between teacher and student — the cleaner, more peaked distributions generated at low temperature align better with what a small model can currently express
Provides a stable foundation — the model first learns the most consistent and representative reasoning patterns before being exposed to more diverse trajectories
Boosts early performance rapidly — low-temperature data provides an efficient jump-start for math and scientific reasoning benchmarks
Mitigates exposure bias — by gradually introducing complexity, the model avoids overfitting to noisy or outlier reasoning traces
This is the key insight behind DASD's temperature-scheduled learning: start cold for stability, then warm up for diversity.
Building on DASD-4B-Thinking-2507-stage1, Stage 2 SFT was performed using the default-temperature subset (T=1.0) of the same dataset. Higher-temperature data introduces greater lexical diversity and broader mode coverage, enabling the model to generalize better across diverse reasoning patterns and problem domains.
This model is intended for research and educational purposes related to reasoning and mathematical problem-solving.
While mathematical and logical reasoning capabilities have been enhanced, the model may still produce incorrect answers — always verify outputs on critical tasks.
The model inherits the capabilities and limitations of the underlying Qwen3-4B-Thinking-2507 architecture.
Not intended for deployment in high-stakes applications without additional safety evaluation.
