Researchers Cut an AI Model to 60B Parameters and Saw It Beat Its Full-Precision Peer

A research team says it has built a smaller, cheaper version of OpenAI’s GPT-OSS 120B and, against normal expectations, ended up with a model that outperformed a comparable full-precision 60-billion-parameter version on most benchmark tests. The approach, described in a paper published Friday, is called Quantization-Aware Healing. Instead of training the compressed model to imitate an already reduced intermediate model, the method points the smaller system back to the original uncompressed teacher model and uses that as the supervision target. The team reduced GPT-OSS from 120 billion parameters to 60 billion and quantized the model to 4-bit precision, a level of compression that would usually come with a meaningful loss in quality. On 7 of 9 tests, the resulting 4-bit 60B model beat the higher-precision 60B counterpart, though the original 120B model still won most comparisons overall. The researchers said the healed model uses roughly a quarter of the memory of the original and half the parameters, lowering hardware demands for deployment. The model, Hypernova-60B, has been released as open weights on Hugging Face. Still, the underlying shrinking tool remains proprietary, and the team said its testing so far covers GPT-OSS only, not Llama, Qwen, or Mistral families.