AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions. However, this expressiveness also exposes a key challenge in heterogeneous datasets: generative policies can reproduce unreliable action modes whose return distributions...
This work revisits where capacity should be invested in an offline actor--critic method and proposes LAC (Light Actor, deep Critic), a lightweight deterministic actor that matches the strongest diffusion- and flow-matching baselines while achieving up to 4x lower inference latency.
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