3 | @DecagonAI
RL for a Production Agent at Hundreds of Billions of Parameters
Cyrus Asgari on synthetic data, reward design, and stabilizing RL under training-inference mismatch to build better customer-facing agents.
Decagon explains RL for a production customer-facing agent at hundreds of billions of parameters
AIDecagon's Cyrus Asgari discusses synthetic data, reward design, and stabilizing reinforcement learning under training-inference mismatch for customer-facing agents. The post presents these methods as ways to build better production agents, with no model release or benchmark results given.
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