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cs.LG2026
Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving
Yiming Wang, Yikang Liu, Qingyuan Tian +4
Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels. Existing ensemble-based methods construct reward references fro…
cs.LG2026
On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiency
Yiming Wang, Zhuosheng Zhang, Rui Wang
Parallel thinking improves LLM reasoning through multi-path sampling and aggregation. In standard evaluations, due to a lack of sample-specific priors, all samples share a global b…