explore-exploit 1large language models 1mathematical reasoning 1prompt sampling 1reinforcement learning 1
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cs.CL2026
LEEPS: Latent-Guided Explore-Exploit Prompt Sampling for Efficient RLVR in Large Language Models
Shuang Liang, Haoyang Zhou, Yifan Gong +2
The paper introduces LEEPS, a latent-guided explore‑exploit prompt sampler that selects prompts before rollout to reduce wasted generation budget and improve reinforcement learning…
cs.CL2026
TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM
Haoyang Zhou, Li Kong, Shijie Ren +4
Diffusion large language models (dLLMs) offer a promising paradigm for parallel text generation, but in practice they face an accuracy-parallelism trade-off, where increasing token…