mlsanityData to 14 Oct 2026

L0: Reinforcement Learning to Become General Agents

arXiv 2506.23667 ↗First posted 30 Jun 2025cs.CLcited by 4

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

also in 1 more
Abstract (arXiv)

Training large language models (LLMs) to act as autonomous agents for multi-turn, long-horizon tasks remains significant challenges in scalability and training efficiency. To address this, we introduce L-Zero (L0), a scalable, end-to-end training pipeline for general-purpose agents. Featuring a low-cost, extensible, and sandboxed concurrent agent worker pool, L0 lowers the barrier for applying reinforcement learning in complex environments. We also introduce NB-Agent, the agent scaffold within L0, which operates in a "code-as-action" fashion via a Read-Eval-Print-Loop (REPL). We evaluate L0 on factuality question-answering benchmarks. Our experiments demonstrate that a base model can develop robust problem-solving skills using solely Reinforcement Learning with Verifiable Rewards (RLVR). On the Qwen2.5-7B-Instruct model, our method boosts accuracy on SimpleQA from 30 % to 80 % and on HotpotQA from 22 % to 41 %. We have open-sourced the entire L0 system, including our L0 series models, the NB-Agent, a complete training pipeline, and the corresponding training recipes on (https://github.com/cmriat/l0).

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 14 references that are in the map. First posted on arXiv 30 Jun 2025.

Builds on

12 of 14 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
  2. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning24 Apr

Other references · 10

  1. 2025Qwen3 Technical Reportcited by 8,820
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  5. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  6. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  7. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  8. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  9. 2024Executable Code Actions Elicit Better LLM Agentscited by 564
  10. 2025Search-o1: Agentic Search-Enhanced Large Reasoning Modelscited by 433

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
  2. 2026Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learningcited by 9
  3. 2025Towards Agentic Self-Learning LLMs in Search Environmentcited by 8
  4. 2026Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025AgentFly: Extensible and Scalable Reinforcement Learning for LM Agents
  2. 2026Rethinking Agentic Reinforcement Learning In Large Language Models
  3. 2025Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning
  4. 2025R-Zero: Self-Evolving Reasoning LLM from Zero Data
  5. 2023Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
  6. 2026Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
  7. 2024Words as Beacons: Guiding RL Agents with High-Level Language Prompts
  8. 2025A Survey of Reinforcement Learning for Large Reasoning Models