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Harbin Institute of Technology
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RL agents can learn far more efficiently by incorporating group-level natural language feedback, achieving 2.2x sample efficiency gains in sparse-reward environments.
LLM personalities can be steered with fine-tuning-level precision, compositionality, and context-awareness, all without training, by directly manipulating activation vectors in representation space.
