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Author

Amir Hassan Shariatmadari

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Book Open access Aug 2026

InfRL: Inference-time Reinforcement Learning for Research Idea Optimization

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.

Sikun Guo, Amir Hassan Shariatmadari, Jiuqi Wang et al. · 0 citations
Book Open access Aug 2026

InfRL: Inference-time Reinforcement Learning for Research Idea Optimization

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...

Sikun Guo, Amir Hassan Shariatmadari, Jiuqi Wang et al. · 0 citations

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