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Wonbeen Oh

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NeurIPS Conference 2025 Conference Paper

AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners

  • Reiss Koh
  • Wonbeen Oh
  • Jaein Jang
  • MinHyung Lee
  • Hyeongjin Kim
  • Ah Kim
  • Joonkee Kim
  • Junghyun Lee

Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (LMs). The self-improving mechanism often employs random observation (data) sampling. However, this results in trained observation imbalance; inefficiently over-training on solved examples while under-training on challenging ones. In response, we introduce Adaptive STaR (AdaSTaR), a novel algorithm that rectifies this by integrating two adaptive sampling principles: (1) Adaptive Sampling for Diversity: promoting balanced training across observations, and (2) Adaptive Sampling for Curriculum: dynamically adjusting data difficulty to match the model's evolving strength. Across six benchmarks, AdaSTaR achieves best test accuracy in all instances (6/6) and reduces training FLOPs by an average of 58. 6\% against an extensive list of baselines. These improvements in performance and efficiency generalize to different pre-trained LMs and larger models, paving the way for more efficient and effective self-improving LMs.

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ICLR Conference 2025 Conference Paper

FlickerFusion: Intra-trajectory Domain Generalizing Multi-agent Reinforcement Learning

  • Woosung Koh
  • Wonbeen Oh
  • Siyeol Kim
  • Suhin Shin
  • Hyeongjin Kim
  • Jaein Jang
  • Junghyun Lee
  • Se-Young Yun

Multi-agent reinforcement learning has demonstrated significant potential in addressing complex cooperative tasks across various real-world applications. However, existing MARL approaches often rely on the restrictive assumption that the number of entities (e.g., agents, obstacles) remains constant between training and inference. This overlooks scenarios where entities are dynamically removed or $\textit{added}$ $\textit{during}$ the inference trajectory—a common occurrence in real-world environments like search and rescue missions and dynamic combat situations. In this paper, we tackle the challenge of intra-trajectory dynamic entity composition under zero-shot out-of-domain (OOD) generalization, where such dynamic changes cannot be anticipated beforehand. Our empirical studies reveal that existing MARL methods suffer $\textit{significant}$ performance degradation and increased uncertainty in these scenarios. In response, we propose FlickerFusion, a novel OOD generalization method that acts as a $\textit{universally}$ applicable augmentation technique for MARL backbone methods. FlickerFusion stochastically drops out parts of the observation space, emulating being in-domain when inferenced OOD. The results show that FlickerFusion not only achieves superior inference rewards but also $\textit{uniquely}$ reduces uncertainty vis-à-vis the backbone, compared to existing methods. Benchmarks, implementations, and model weights are organized and open-sourced at $\texttt{\href{flickerfusion305.github.io}{\textbf{flickerfusion305.github.io}}}$, accompanied by ample demo video renderings.

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v2026.09.13