AgenticRAG-R1: RL Framework with Stack Memory for Reasoning Agents
_reachsumit · x · 2026-09-01
The paper proposes AgenticRAG-R1, an RL framework for training RAG agents.
- Innovation: Introduces a "stack memory" and fine-grained action space (plan, search, backtrack) to deeply integrate reasoning, retrieval, and memory.
- Solution: Addresses weak reward assignment and short-horizon bias in existing RL methods that rely on coarse-grained actions and trajectory-level rewards.
- Performance: Outperforms baselines on multi-hop, open-domain, and agentic reasoning benchmarks, learning more robust and interpretable behaviors.
More from coding & agent
- Introducing Loupe: An open-source PR review agent — andersonbcdefg · 2026-09-01
- Why "it feels better" isn't good enough for production LLM decisions — camerongreen95 · 2026-09-01
- Hermes Agent monitors chats and emails to auto-create tasks and draft work — intellectronica · 2026-09-01
- Free Australian business-day MCP server released — Impossible_Bit_2676 · 2026-09-01
- If starting with AI agents today, what would you automate first? — omnidimension85 · 2026-09-01
- SparkLLM open-sources X2.5 4B/1.7B on-device models with native 1M-token context — vllm_project · 2026-09-01