AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
3.00T1 sourcearXiv cs.MA
arXiv cs.MAoriginal source ↗
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.29622.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA reinforcement learning framework, AgenticRag-R1, that integrates reasoning, retrieval, and memory via a memory stack and fine-grained action space with hierarchical action-aware rewards for long-horizon RAG tasks, outperforming baselines across multi-hop and agentic reasoning benchmarks.
Why it mattersStack-memory design and fine-grained reward shaping target known weak spots in agentic RAG, with code released for replication and benchmarking.
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