arXiv cs.CLPaper
Beyond Repeated Sampling: Learning Search Policies for LLM Reasoning
This moves beyond brute-force compute scaling by making exploration strategic. If you're building reasoning systems, this shows how to extract more value from test-time compute by learning what kinds of semantic prompts help the base model succeed. The training loop is tractable for teams with infrastructure. Worth a prototype if you're already hitting limits on beam search.