Researchers at MetaStone-AI and the University of Science and Technology of China have introduced MetaStone-S1, a reflective generative model that matches OpenAI’s o3-mini performance with a design called Reflective Generative Form.
The system merges the policy network, which generates reasoning paths, with a step-level Process Reward Model (PRM) under a single parameter set. This requires only 53 million extra parameters for the verifier in the 32 billion parameter version, compared with tens of billions in standalone PRMs.
The self-supervised Process Reward Model removes the need for process-level labels. Its loss function judges intermediate reasoning steps using only the correctness of the final result. A dynamic weight filter deals with label noise.
Rather than boosting performance through model size alone, MetaStone-S1 uses test-time sampling (TTS) to gain accuracy from extra computation during inference. Internal TTS deepens chain-of-thought sequences at the cost of higher compute loads. External TTS runs multiple reasoning threads in parallel and picks the top one via the PRM.
MetaStone-S1 blends both modes in one architecture, offering accurate path selection with minimal overhead.
The architecture runs at 1.5 billion, 7 billion, and 32 billion parameter scales. The 32 billion version matches or beats major closed and open models, including o3-mini, on math and reasoning benchmarks.
Each scale shows strong growth with the TTS method. The smallest outperforms peers on math tests, and the larger models handle harder tasks well.
Overhead stays low. The SPRM adds only about 26 million parameters versus 72 billion for a typical PRM, yet it achieves top scores across benchmarks.
Training logs reveal a clear tipping point when the model learns to score correct versus incorrect paths, boosting its discrimination ability.
Performance follows a logarithmic growth with the total compute budget (model size × reasoning tokens), leveling off around Best-of-32 sampling.
Users can choose from three test-time sampling modes: Low (k = 2) for speed, Medium (k = 8) for balance, and High (k = 32) for deep reasoning on complex queries.
MetaStone-S1’s unified structure merges problem solving and verification in one network. It matches o3-mini’s results with far fewer resources, showing that smarter design can rival brute-force scaling and widen access to advanced AI reasoning.
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