Education
Stepwise Alignment for Constrained Language Model Policy Optimization Akifumi Wachi Thien Q. Tran Rei Sato Takumi Tanabe Y ouhei Akimoto L Y Corporation University of Tsukuba
Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs).
Knowledge Distillation Performs Partial Variance Reduction
Knowledge distillation is a popular approach for enhancing the performance of "student" models, with lower representational capacity, by taking advantage of
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
Taking an example of hyperspectral image reconstruction, the spectral SCI [Gehm et al., 2007] can fast capture and compress 3D hyperspectral signals as
Reinforcing LLM Agents via Policy Optimization with Action Decomposition
Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization.
LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts.