4 accepted papers
Recent advances in large language models (LLMs) have broadened their applicability across diverse tasks, yet specialized domains still require targeted post-training. Among existing methods, Group Relative Policy Optimization (GRPO) stands out for its efficiency, leveraging groupwise relative reward
Training stability remains a critical bottleneck for Group Relative Policy Optimization (GRPO), often manifesting as a trade-off between reasoning plasticity and general capability retention. We identify a root cause as the geometric conflict between plasticity and stability gradients, which leads t…
The pre-trained models contain multitudinous knowledge from huge amount of data. However, when applying these models to downstream tasks, they may mis-locate to wrong knowledge distribution due to a lack of domain or contextual knowledge. To address the distribution bias between the pre-trained mode…
Recent advances in cross-technology communication (CTC) enable heterogeneous wireless devices (e.g., WiFi, Zig-Bee, and BLE) operating in the ISM band to communicate and understand each other. However, due to the limitation of standards and devices, existing CTC techniques need to design specific sc…