ACING: Actor-Critic for Instruction Learning in Black-Box LLMs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities across tasks like classification, summarization, and reasoning. |
| Approach: | They propose an actor-critic reinforcement learning framework that formulates instruction optimization as a stateless, continuous-action problem. |
| Outcome: | The proposed framework outperforms human-written prompts in 76% of instruction-induction tasks with gains of 33 points and 10-point improvement over baseline. |
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