2025
Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
Yifan Zhou, Sachin Grover, Mohamed El Mistiri, Kamalesh Kalirathinam, Pratyush Kerhalkar, Swaroop Mishra +4
NeurIPS 2025poster
Reinforcement Learning (RL) traditionally relies on scalar reward signals, limiting its ability to leverage the rich semantic knowledge often available in real-world tasks. In contrast, humans learn efficiently by combining numerical feedback with language, prior knowledge, and common sense. We intr…