6 papers
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Xinyu Tang, Qianggang Cao, Yurou Liu +13
Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoni…
Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences
Mingyang Li, Yurou Liu, Jieping Ye +3
In this report, we present LOGOS (Language Of Generative Objects in Science), a scientific generative language model that unifies heterogeneous tasks across the natural sciences wi…
Probing RLVR training instability through the lens of objective-level hacking
Yiming Dong, Kun Fu, Haoyu Li +5
Prolonged reinforcement learning with verifiable rewards (RLVR) has been shown to drive continuous improvements in the reasoning capabilities of large language models, but the trai…
Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling
Xixian Liu, Rui Jiao, Zhiyuan Liu +6
Coordinate denoising has emerged as a promising method for 3D molecular pretraining due to its theoretical connection to learning molecular force field. However, existing denoising…
Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward
Xinyu Tang, Zhenduo Zhang, Yurou Liu +4
Recent advances in large reasoning models have leveraged reinforcement learning with verifiable rewards (RLVR) to improve reasoning capabilities. However, scaling these methods typ…
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning
Jiahao Chen, Yurou Liu, Jiangmeng Li +2
Molecular representation learning is a crucial task in predicting molecular properties. Molecules are often modeled as graphs where atoms and chemical bonds are represented as node…