Intern-S2-Preview: 397B scientific agentic foundation model from Shanghai AI Lab
Shanghai AI Lab
A 397B scientific agentic foundation model trained over rendered scientific documents, interleaved image-text, and scientific corpora, then post-trained with SFT, scalable multi-task RL, black- and white-box agentic RL, and on-policy distillation — stabilized by partial rollout with off-policy correction, adaptive length regularization, online speculative decoding, and trace-aware experience assembly. Pairs the frozen 397B backbone with a 4B Memory Decoder for rapid scientific specialization, and adds a dedicated time-series module for SciTS forecasting.
Why it matters
47 upvotes on HF Daily Papers; a serious open-weights-class scientific agentic foundation model from a major Chinese lab, with explicit agentic-RL training and a parameter-isolated extension path that avoids retraining the backbone.
Importance: 3/5
397B scientific agentic foundation model from Shanghai AI Lab / InternLM team.