In-the-Flow Agentic System Optimization for Effective Planning and Tool
  Use
Authors: name: Zhuofeng Li, name: Haoxiang Zhang, name: Seungju Han, name: Sheng Liu, name: Jianwen Xie, name: Yu Zhang, name: Yejin Choi, name: James Zou, name: Pan Lu
Organization: Stanford AI

Outcome-driven reinforcement learning has advanced reasoning in large
language models (LLMs), but prevailing tool-augmented approaches train a
single, monolithic policy that interleaves thoughts and tool calls under full
context; this scales poorly with long horizons and diverse tools and
generalizes weakly to new scenarios. Agentic systems offer a promising
alternative by decomposing work across specialized modules, yet most remain
training-free or rely on offline training decoupled from the live dynamics of
multi-turn interaction. We introduce AgentFlow, a trainable, in-the-flow
agentic framework that coordinates four modules (planner, executor, verifier,
generator) through an evolving memory and directly optimizes its planner inside
the multi-turn loop. To train on-policy in live environments, we propose
Flow-based Group Refined Policy Optimization (Flow-GRPO), which tackles
long-horizon, sparse-reward credit assignment by converting multi-turn
optimization into a sequence of tractable single-turn policy updates. It
broadcasts a single, verifiable trajectory-level outcome to every turn to align
local planner decisions with global success and stabilizes learning with
group-normalized advantages. Across ten benchmarks, AgentFlow with a 7B-scale
backbone outperforms top-performing baselines with average accuracy gains of
14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on
scientific tasks, even surpassing larger proprietary models like GPT-4o.
Further analyses confirm the benefits of in-the-flow optimization, showing
improved planning, enhanced tool-calling reliability, and positive scaling with
model size and reasoning turns.