Monica Xiao Cheng

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, methods such as GRPO and DAPO suffer from substantial computational cost, since they rely on sampling many rollouts for each prompt. Moreover, in R...

Personalizing large language models (LLMs) to individual users requires incorporating extensive interaction histories and profiles, but input token constraints make this impractical due to high inference latency and API costs. Existing approaches rely on heuristic methods such as selecting recent...

Deploying a conversational shopping agent at production scale remains challenging despite rapid advances in large language models. Unlike research prototypes, production systems must satisfy strict requirements on accuracy, latency, reliability, and cost while serving millions of customers. We sh...