Maximizing Rollout Informativeness under a Fixed Budget: A Submodular View of Tree Search for Tool-Use Agentic Reinforcement Learning
arXiv 2605.05262•0ba6f4bfb93f433437aa38ba73176e32f046518eb9415faff47ae9d8ea8d00bf
arxivbayesiancausal-inferencedeep-learninggaussian-processesgenerative-modelsllm-diagnosticsmachine-learningno_cvereinforcement-learningresearch-collectionscalabilitystochastic-dynamicstheorytime-series
Paper metadata
- arXiv ID
- 2605.05262
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 0ba6f4bfb93f433437aa38ba73176e32f046518eb9415faff47ae9d8ea8d00bf
- Enrichment time
- 2026-05-08T07:24:05Z
- AI-assisted enrichment
- Yes
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