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We study offline learning in two-player zero-sum contextual games with public and private information, motivated by strategic settings such as auctions and negotiations with private valuations. We introduce unilateral prescriptive concentrability and show that asymmetric information can change offline coverage through...
This paper develops a machine learning approach for training such agents to act effectively as sellers in a multi-item bargaining environment, where a seller concurrently negotiates a catalog of substitutable assets across a pool of independent buyers.
InfRL (Inference-time Reinforcement Learning) offers a practical and domain-agnostic approach to harness reinforcement learning during inference, bridging the gap between static prompting and computationally intensive parameter-level fine-tuning.
Large language models (LLMs) possess extensive latent knowledge yet remain largely static at inference. Once prompted, their generation policy typically cannot evolve, and post-hoc ''self-reflection'' methods provide no explicit principled learning signals. To address this limitation, we formally model iterative resear...
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