Study compares RL design choices for agentic reasoning

Published on October 19, 2025, the article systematically investigates reinforcement learning (RL) design choices for agentic reasoning in language models. Its scope includes data, algorithms, and reasoning modes, aiming to inform the training of agents that reason and use tools.

The preview highlights a comparison between concatenated synthetic trajectories and real end-to-end tool-use trajectories, but does not report specific findings. To consult and verify the conclusions, read the original article and check how it defines the trajectories, methods, and evaluations; do not infer more than the preview states.