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Agents can recursively improve their problem-solving by learning to adapt their own policy and memory management strategies, achieving substantial error reduction in program selection.
A low-rank residual connection allows frozen LLMs to learn from feedback with minimal parameters, achieving substantial error reduction in downstream tasks.