We have 3 of 5 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A series of models are explored that connect item Semantic IDs with natural language in a unified representation space and seek to unlock the potential of natural-language chain-of-thought (CoT) reasoning for recommendation, including OneReason.
These results show that RecoReward trains the MLLM to produce item features that benefit downstream recommendation while retaining content-only serving, and shows that RecoReward-9B outperforms its Qwen3.5-9B baseline and all other evaluated models across seven recall metrics.
The sample efficiency and scalability of RL post-training for video MLLMs and introduces OraRL, a decoupled advantage estimator that scales with model size and data, surpassing its backbone from 0.8B to 9B and GRPO up to 100k prompts.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
