AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A three-stage reinforced fine-tuning (RFT) pipeline for multi-agent VLAs is presented, which freezes the VLA and performs RL in its latent noise space and finds existing online RL for VLAs are less effective for hard multi-agent tasks, which are attributed to noisy co-exploration and unstable updates.
This paper examines whether synthetic training artifacts can support the complete SFT-to-RL cycle for competitive programming, and synthesizes tasks, verified solutions, and reliable test cases that serve as reward signals for reinforcement learning.
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