most citedPass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

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cs.LG2026

LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling

Jian Yang, Shawn Guo, Wei Zhang +16

Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop T…

cs.LG2026

Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization

Fei Bai, Zhipeng Chen, Chuan Hao +6

Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from ve…

cs.LG2025★ 1 cited

Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models

Zhipeng Chen, Xiaobo Qin, Youbin Wu +4

Reinforcement learning with verifiable rewards (RLVR), which typically adopts Pass@1 as the reward, has faced the issues in balancing exploration and exploitation, causing policies…