01 / Research agenda
Self-evolving LLMs.
I study how language models can continually improve through reasoning, interaction, feedback, and the data they generate.
Reasoning & RL
Improving multi-step reasoning capabilities through scalable reinforcement learning and exploration.
Data synthesis
Creating scalable synthetic data, adaptive environments, and curricula for continual model improvement.
Verifier & feedback
Designing reliable evaluation and feedback signals that close the loop between generation and learning.
02 / Updates
Recent news
| Sep 11, 2026 | 🚀 We released Atria-Dawn Preview, a 744B-parameter MoE model with a 256K-token context window for research and engineering — combining tool use, coding, and multi-step experimentation to produce verifiable, reproducible results. Model weights · Technical report. |
|---|---|
| Sep 11, 2026 | 🤗 SCALER has been included in Hugging Face’s Repo2RLEnv! |
| May 27, 2026 | 📄 New preprint: DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes — learning to recover from weak-model failures without a stronger teacher. |
| May 04, 2026 | 📄🎉 SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning — accepted to ACL 2026 Findings. |
| Oct 20, 2024 | 📄🎉 Exploring High-Order User Preference with Knowledge Graph for Recommendation — accepted to CIKM 2024 (short paper). |
| Dec 03, 2023 | 🥉 Won ICPC Bronze Medal (Jinan). |
03 / Selected work
Publications
- CIKM’24Exploring High-Order User Preference with Knowledge Graph for RecommendationIn Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, Jan 2024
04 / Contact
Let’s exchange ideas.
I’m always glad to discuss self-evolving LLMs, reasoning, reinforcement learning, and synthetic data.