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LLM agents achieve substantial performance gains by jointly learning credit at both the turn and token generation levels, overcoming limitations of existing single-level credit assignment methods.
ALMANAC reveals that agents can significantly improve their collaborative competence by learning from detailed human mental model annotations.
LLM agents often struggle not due to a lack of reasoning skills, but because they fail to collaborate effectively, as revealed by the new CollabSim framework.
