Ziliang Wang

E^3RL not only overcomes the autoregressive curse but also enhances LLMs' reasoning capabilities, achieving up to 6.514% better performance than previous state-of-the-art models.

Semantic ID quality hinges on a delicate balance of robustness and fidelity, with the new DRQ method offering a fresh lens on tokenizer performance.

LLMs can significantly boost their emotional intelligence simply by role-playing conversations with themselves, iteratively refining their ability to both recognize and express emotions.

Generative recommendation models can match the expressiveness of discriminative models by explicitly incorporating item attribute information during sequence decoding, leading to substantial performance gains.