In a nutshell: FlowTracer assigns credit to tokens based on their measured information throughput in the attention graph rather than treating all equally, yielding consistent performance gains in reasoning tasks.
Researchers introduce FlowTracer, an RL framework that evaluates tokens in large language models based on their role in information flow, enabling more precise credit assignment for complex reasoning processes. The method tracks which tokens actually convey information from input to correct answer.
The central challenge in reinforcement learning with large language models is determining which individual tokens are critical for correct answers. Previous RL approaches treat all tokens equally or rely on isolated internal signals — but ignore how information actually flows through the model. FlowTracer addresses this through a global perspective: the method constructs a directed acyclic graph in which tokens form the nodes and edge weights are derived from aggregated attention values.
The edges are then reweighted so that only influences that actually reach the answer region are retained. Through local flow conservation, intermediate tokens are ensured not to lose effective information through path lengths or irrelevant branching. The algorithm extracts from this an information-flow skeleton connecting question and answer, and evaluates tokens based on their throughput — with the goal of identifying high-impact hubs and aggregation points that mediate cross-cutting dependencies.
Source: arxiv.org · Published June 9, 2026
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