Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
Authors: name: Junhong Shen, name: Hao Bai, name: Lunjun Zhang, name: Yifei Zhou, name: Amrith Setlur, name: Shengbang Tong, name: Diego Caples, name: Nan Jiang, name: Tong Zhang, name: Ameet Talwalkar, name: Aviral Kumar

The current paradigm of test-time scaling relies on generating long reasoning
traces ("thinking" more) before producing a response. In agent problems that
require interaction, this can be done by generating thinking traces before
acting in the world. However, this process does not allow agents to acquire new
information from the environment or adapt their behavior over time. In this
work, we propose to scale test-time interaction, an untapped dimension of
test-time scaling that increases the agent's interaction horizon to enable
running rich behaviors such as exploration, backtracking, and dynamic
re-planning within a single rollout. To demonstrate the promise of this scaling
dimension, we study the domain of web agents. We first show that even
prompting-based interaction scaling without any training can improve task
success on web benchmarks non-trivially. Building on this, we introduce TTI
(Test-Time Interaction), a curriculum-based online reinforcement learning (RL)
approach that trains agents by adaptively adjusting their rollout lengths.
Using a Gemma 3 12B model, TTI produces state-of-the-art open-source, open-data
web agents on WebVoyager and WebArena benchmarks. We further show that TTI
enables agents to balance exploration and exploitation adaptively. Our results
establish interaction scaling as a powerful, complementary axis to scaling
per-step compute, offering new avenues for training adaptive agents.