1 citations · 1 across the 12 of their papers we have counts for
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Entropy-Guided Data-Efficient Training for Multimodal Reasoning Reward Models
Shidong Yang, Tongwen Huang, Hao Wen +3
Multimodal reward models are crucial for aligning multimodal large language models with human preferences. Recent works have incorporated reasoning capabilities into these models,…
Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulation
Yanqi Dai, Yuxiang Ji, Xiao Zhang +3
Reinforcement Learning with Verifiable Rewards (RLVR) offers a robust mechanism for enhancing mathematical reasoning in large models. However, we identify a systematic lack of emph…
Where and What Matters: Sensitivity-Aware Task Vectors for Many-Shot Multimodal In-Context Learning
Ziyu Ma, Chenhui Gou, Yiming Hu +4
Large Multimodal Models (LMMs) have shown promising in-context learning (ICL) capabilities, but scaling to many-shot settings remains difficult due to limited context length and hi…
HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation
Feng Xiong, Hongling Xu, Yifei Wang +3
Self-taught reasoners (STaRs) enhance the mathematical reasoning abilities of large language models (LLMs) by leveraging self-generated responses for self-training. Recent studies…