TL;DR: AgentFlow is a trainable agent framework with four modules—Planner, Executor, Verifier, Generator—coordinated by an explicit memory and toolset. The planner is optimized in the loop with a new on-policy method, Flow-GRPO, which broadcasts a trajectory-level outcome reward to every turn and applies token-level PPO-style updates with KL regularization and group-normalized advantages. On ten benchmarks, a 7B backbone tuned with Flow-GRPO reports +14.9% (search), +14.0% (agentic), +14.5% (math), and +4.1% (science) over strong baselines.

What is AgentFlow?

AgentFlow formalizes multi-turn, tool-integrated reasoning as an Markov Decision Process (MDP). At each turn, the Planner proposes a sub-goal and selects a tool plus context; the Executor calls the tool; the Verifier signals whether to continue; the Generator emits the final answer on termination. A structured, evolving memory records states, tool calls, and verification signals, constraining context growth and making trajectories auditable. Only the planner is trained; other modules can be fixed engines.

The public implementation showcases a modular toolkit (e.g., base_generator, python_coder, google_search, wikipedia_search, web_search) and ships quick-start scripts for inference, training, and benchmarking. The repository is MIT-licensed.

https://arxiv.org/pdf/2510.05592

Training method: Flow-GRPO

Flow-GRPO (Flow-based Group Refined Policy Optimization) converts long-horizon, sparse-reward optimization into tractable single-turn updates:

Final-outcome reward broadcast: a single, verifiable trajectory-level signal (LLM-as-judge correctness) is assigned to every turn, aligning local planning steps with global success.

Token-level clipped objective: importance-weighted ratios are computed per token, with PPO-style clipping and a KL penalty to a reference policy to prevent drift.

Group-normalized advantages: variance reduction across groups of on-policy rollouts stabilizes updates.

https://arxiv.org/pdf/2510.05592

Understanding the results and benchmarks

Benchmarks. The research team evaluates four task types: knowledge-intensive search (Bamboogle, 2Wiki, HotpotQA, Musique), agentic reasoning (GAIA textual split), math (AIME-24, AMC-23, Game of 24), and science (GPQA, MedQA). GAIA is a tooling-oriented benchmark for general assistants; the textual split excludes multimodal requirements.

Main numbers (7B backbone after Flow-GRPO). Average gains over strong baselines: +14.9% (search), +14.0% (agentic), +14.5% (math), +4.1% (science). The research team state their 7B system surpasses GPT-4o on the reported suite. The project page also reports training effects such as improved planning quality, reduced tool-calling errors (up to 28.4% on GAIA), and positive trends with larger turn budgets and model scale.

Ablations. Online Flow-GRPO improves performance by +17.2% vs. a frozen-planner baseline, while offline supervised fine-tuning of the planner degrades performance by −19.0% on their composite metric.

https://arxiv.org/pdf/2510.05592

Key Takeaways

Modular agent, planner-only training. AgentFlow structures an agent into Planner–Executor–Verifier–Generator with an explicit memory; only the Planner is trained in-loop.

Flow-GRPO converts long-horizon RL to single-turn updates. A trajectory-level outcome reward is broadcast to every turn; updates use token-level PPO-style clipping with KL regularization and group-normalized advantages.

The research team-reported gains on 10 benchmarks. With a 7B backbone, AgentFlow reports average improvements of +14.9% (search), +14.0% (agentic/GAIA textual), +14.5% (math), +4.1% (science) over strong baselines, and states surpassing GPT-4o on the same suite.

Tool-use reliability improves. The research team report reduced tool-calling errors (e.g., on GAIA) and better planning quality under larger turn budgets and model scale.

Editorial Comments

AgentFlow formalizes tool-using agents into four modules (planner, executor, verifier, generator) and trains only the planner in-loop via Flow-GRPO, which broadcasts a single trajectory-level reward to every turn with token-level PPO-style updates and KL control. Reported results on ten benchmarks show average gains of +14.9% (search), +14.0% (agentic/GAIA textual split), +14.5% (math), and +4.1% (science); the research team additionally state the 7B system surpasses GPT-4o on this suite. Implementation, tools, and quick-start scripts are MIT-licensed in the GitHub repo.

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