SERO trains agentic RL on executable oracle continuations
Read original source →arxiv.orgSERO improves long-horizon agentic reinforcement learning by training on executable oracle continuations from real policy states, weighted by policy compatibility, with no inference-time overhead. Evaluated on ALFWorld and WebShop.
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