Wenwei Zhang's picture

Wenwei Zhang

ZwwWayne

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Recent Activity

liked a model 3 days ago

internlm/Intern-S1-Pro

authored a paper about 2 months ago

Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving

upvoted a paper about 2 months ago

Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning

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1568

Here, we share the technical details of InternLM2, the current state-of-the-art open-source LLMs!!

See collections internlm/internlm2-65b0ce04970888799707893c
Paper: InternLM2 Technical Report (2403.17297)

Post

1782

How to let LLM acquire the Agentic Capability?

Previous answers are direct imitation learning by collecting agentic data such as tool calling history (inefficient and introduces format hallucination).
Agent-FLAN tells a different view:
- Eliciting the foundational capability (e.g., reasoning, retrieval, and instruction following) is more important
- Using chat data is more effective with less side effects than tool calling history

Dataset: internlm/Agent-FLAN
HF Model: internlm/Agent-FLAN-7b
Paper: Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models (2403.12881)
Project page：https://internlm.github.io/Agent-FLAN/

Papers 53

arxiv:2512.10739

arxiv:2508.15763

arxiv:2508.08636

arxiv:2508.03686

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ZwwWayne (Wenwei Zhang)

Wenwei Zhang's picture

Wenwei Zhang

ZwwWayne

·

AI & ML interests

None yet

Recent Activity

liked a model 3 days ago

internlm/Intern-S1-Pro

authored a paper about 2 months ago

Long-horizon Reasoning Agent for Olympiad-Level Mathematical Problem Solving

upvoted a paper about 2 months ago

Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning

View all activity

Organizations

Posts 2

Post

1568

Here, we share the technical details of InternLM2, the current state-of-the-art open-source LLMs!!

See collections internlm/internlm2-65b0ce04970888799707893c
Paper: InternLM2 Technical Report (2403.17297)

Post

1782

How to let LLM acquire the Agentic Capability?

Previous answers are direct imitation learning by collecting agentic data such as tool calling history (inefficient and introduces format hallucination).
Agent-FLAN tells a different view:
- Eliciting the foundational capability (e.g., reasoning, retrieval, and instruction following) is more important
- Using chat data is more effective with less side effects than tool calling history

Dataset: internlm/Agent-FLAN
HF Model: internlm/Agent-FLAN-7b
Paper: Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models (2403.12881)
Project page：https://internlm.github.io/Agent-FLAN/

Papers 53

arxiv:2512.10739

arxiv:2508.15763

arxiv:2508.08636

arxiv:2508.03686

models 0

None public yet

datasets 0

None public yet