3 papers
cs.DC2026
AuroraRL: Fast, Fault-Tolerant, and Cost-Efficient Reinforcement Learning over Decentralized Network
Chaoyi Ruan, Geng Luo, Xinyi Wan +12
LLM reinforcement learning (RL) requires frequent synchronization of large model parameters between the trainer and distributed rollout actors. High-throughput RL post-training the…
cs.LG2026
Internalizing LLM Reasoning via Discovery and Replay of Latent Actions
Zhenning Shi, Yijia Zhu, Junhan Shi +3
The internalization of chain-of-thought processes into hidden states has emerged as a highly efficient paradigm for scaling test-time compute. However, existing activation steering…
cs.NI2025
Automated Network Protocol Testing with LLM Agents
Yunze Wei, Kaiwen Wei, Shibo Du +7
Network protocol testing is fundamental for modern network infrastructure. However, traditional network protocol testing methods are labor-intensive and error-prone, requiring manu…