Zixian Huang

Intern-S2-Preview-397B not only excels in multimodal scientific reasoning but also enhances biological instruction performance without altering its foundational architecture.

Automating data selection with DataMaster not only reduces manual effort but also enhances performance across diverse applications, challenging traditional heuristic methods.

LLMs can now automatically evolve and optimize GPU kernels to beat hand-tuned and proprietary models like Gemini and Claude.

Fine-tuning smaller reasoning models on data from larger models can backfire spectacularly unless you carefully match the stylistic nuances of the student.

Multilingual reasoning in LLMs isn't just about translation—it's a powerful knob for improving RL training by expanding the exploration space and boosting exploitation.