Intern-S2-Preview: Scientific Agentic Foundation Model
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TL;DR - Intern-S2-Preview is a scientific agentic foundation-model series for multimodal reasoning, tool use, generation, and long-horizon tasks. Its 397B model reports competitive or leading benchmark performance, with specialized modules improving time-series forecasting and biology tasks.
- Pretraining combines rendered scientific documents, interleaved image-text data, and diverse scientific corpora.
- Post-training integrates supervised fine-tuning, multi-task and agentic reinforcement learning, and on-policy distillation.
- Efficiency and stability techniques include partial rollouts with off-policy correction, speculative decoding, and trace-aware experience assembly.
- A separate 4B Memory Decoder raises the Biology-Instructions average from 56.92 to 60.32 without modifying the frozen 397B backbone.
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Intern-S2-Preview: Scientific Agentic Foundation Model
Public signals
Hugging Face upvotes 72
TL;DR - Intern-S2-Preview is a scientific agentic foundation-model series for multimodal reasoning, tool use, generation, and long-horizon tasks. Its 397B model reports competitive or leading benchmark performance, with specialized modules improving time-series forecasting and biology tasks.
- Pretraining combines rendered scientific documents, interleaved image-text data, and diverse scientific corpora.
- Post-training integrates supervised fine-tuning, multi-task and agentic reinforcement learning, and on-policy distillation.
- Efficiency and stability techniques include partial rollouts with off-policy correction, speculative decoding, and trace-aware experience assembly.
- A separate 4B Memory Decoder raises the Biology-Instructions average from 56.92 to 60.32 without modifying the frozen 397B backbone.