Portrait de Yihao Sun

Yihao Sun

Doctorat - UdeM
Superviseur⋅e principal⋅e
Pierre-Luc Bacon
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Site web
Google Scholar
GitHub

Publications

The Three Regimes of Offline-to-Online Reinforcement Learning
Li Li
Tianwei Ni
Yihao Sun
Pierre-Luc Bacon
Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online… (voir plus) interactions for fine-tuning. However, its empirical behavior is highly inconsistent: design choices of online-fine tuning that work well in one setting can fail completely in another. We propose a stability--plasticity principle that can explain this inconsistency: we should preserve the knowledge of pretrained policy or offline dataset during online fine-tuning, whichever is better, while maintaining sufficient plasticity. This perspective identifies three regimes of online fine-tuning, each requiring distinct stability properties. We validate this framework through a large-scale empirical study, finding that the results strongly align with its predictions in 45 of 63 cases. This work provides a principled framework for guiding design choices in offline-to-online RL based on the relative performance of the offline dataset and the pretrained policy.
2025-10-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Planning with Unified Multimodal Models
Yihao Sun
Zhilong Zhang
Yang Yu
Pierre-Luc Bacon
With the powerful reasoning capabilities of large language models (LLMs) and vision-language models (VLMs), many recent works have explored … (voir plus)using them for decision-making. However, most of these approaches rely solely on language-based reasoning, which limits their ability to reason and make informed decisions. Recently, a promising new direction has emerged with unified multimodal models (UMMs), which support both multimodal inputs and outputs. We believe such models have greater potential for decision-making by enabling reasoning through generated visual content. To this end, we propose Uni-Plan, a planning framework built on UMMs. Within this framework, a single model simultaneously serves as the policy, dynamics model, and value function. In addition, to avoid hallucinations in dynamics predictions, we present a novel approach self-discriminated filtering, where the generative model serves as a self-discriminator to filter out invalid dynamics predictions. Experiments on long-horizon planning tasks show that Uni-Plan substantially improves success rates compared to VLM-based methods, while also showing strong data scalability, requiring no expert demonstrations and achieving better performance under the same training-data size. This work lays a foundation for future research in reasoning and decision-making with UMMs.
2025-09-27
ArXiv (prépublication)
arxiv.org
arxiv.org