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News

[2026-02] I passed my PhD qualifying examination with honors!
[2026-01] Both Skywork-Reward-V2 and DRAGON have been accepted to ICLR 2026!
[2025-09] One paper has been accepted to the NeurIPS 2025 as a Spotlight!
[2025-07] I gave a talk to the Qwen post-training team about Skywork-Reward-V2.
[2025-07] We released the second generation of our reward models, Skywork-Reward-V2, which achieves SOTA performance on 7 reward model benchmarks!
[2025-06] Two papers on LLM unlearning have been accepted to the ICML 2025 Workshop on Machine Unlearning for Generative AI!
[2025-04] We have released the Skywork-OR1 (Open Reasoner 1) series of models, a collection of SOTA 7B and 32B models specialized in math and code. We open-source model weights, data, and training code. We also release a Notion Blog to share detailed training recipes and extensive experimental results, analysis, and insights.
[2025-02] One paper on LLM unlearning has been accepted to the ICLR 2025!
[2024-12] Our review paper Rethinking Machine Unlearning for Large Language Models has been accepted for publication in Nature Machine Intelligence!
[2024-11] We have released our Skywork o1 Open collection on Hugging Face! The collection includes a model with o1-like CoT and two process reward models.
[2024-10] Our technical report Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs has been released on arXiv!
[2024-09] Our paper Large Language Model Unlearning via Embedding-Corrupted Prompts has been accepted to NeurIPS 2024!
[2024-09] We released Skywork-Reward-Gemma-2-27B and Skywork-Reward-Llama-3.1-8B, two state-of-the-art reward models rank 1st and 3rd on the RewardBench leaderboard!
[2024-06] I joined Skywork AI as a Research Intern, where I will be working on alignment for large language models.
[2024-06] We present Embedding-COrrupted (ECO) Prompts, a lightweight unlearning framework for large language models. The paper is available on arXiv and the project page is available here.
[2024-03] I compiled two lists of papers on machine unlearning for large language models and representation engineering.
[2023-12] My paper on understanding the role of optimization in double descent has been accepted to the NeurIPS 2023 Optimization for Machine Learning Workshop. The paper is available on arXiv and the poster is available here.