Anticipatory Planning for Multimodal AI Agents
Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reasoning about future states or long-term goals. This limits planning coherence and prevents agents from reliably solving high-level, multi-step tasks. We introduce TraceR1, a two-stage reinforcement learning framework that explicitly trains anticipatory reasoning by forecasting short-horizon trajectories before execution. The first stage performs trajectory-level reinforcement learning with rewards that enforce global consistency across predicted action sequences. The second stage applies grounded reinforcement fine-tuning, using execution feedback from frozen tool agents to refine step-level accuracy and executability. TraceR1 is evaluated across seven benchmarks, covering online computer-use, offline computer-use benchmarks, and multimodal tool-use reasoning tasks, where it achieves substantial improvements in planning stability, execution robustness, and generalization over reactive and single-stage baselines. These results show that anticipatory trajectory reasoning is a key principle for building multimodal agents that can reason, plan, and act effectively in complex real-world environments.
69ba420f5d2b9c28ea09f243
Yongyuan Liang
69ba420f5d2b9c28ea09f244
Shijie Zhou
69ba420f5d2b9c28ea09f245
Yu Gu
69ba420f5d2b9c28ea09f246
Hao Tan
69ba420f5d2b9c28ea09f247
Gang Wu
69ba420f5d2b9c28ea09f248
Franck Dernoncourt
claimed_verified
2026-03-18T08:35:49.963Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
Franck-Dernoncourt
user
Franck-Dernoncourt
69ba420f5d2b9c28ea09f249
Jihyung Kil
69ba420f5d2b9c28ea09f24a
Ryan A. Rossi
69ba420f5d2b9c28ea09f24b
Ruiyi Zhang
Anticipatory Planning for Multimodal AI Agents
Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reasoning about future states or long-term goals. This limits planning coherence and prevents agents from reliably solving high-level, multi-step tasks. We introduce TraceR1, a two-stage reinforcement learning framework that explicitly trains anticipatory reasoning by forecasting short-horizon trajectories before execution. The first stage performs trajectory-level reinforcement learning with rewards that enforce global consistency across predicted action sequences. The second stage applies grounded reinforcement fine-tuning, using execution feedback from frozen tool agents to refine step-level accuracy and executability. TraceR1 is evaluated across seven benchmarks, covering online computer-use, offline computer-use benchmarks, and multimodal tool-use reasoning tasks, where it achieves substantial improvements in planning stability, execution robustness, and generalization over reactive and single-stage baselines. These results show that anticipatory trajectory reasoning is a key principle for building multimodal agents that can reason, plan, and act effectively in complex real-world environments.
TraceR1 is a two-stage reinforcement learning framework that enhances multimodal agents' planning capabilities through anticipatory trajectory reasoning and execution feedback refinement.
reinforcement learning trajectory forecasting trajectory-level reinforcement learning grounded reinforcement fine-tuning multimodal agents computer-use interaction tool-usage planning coherence execution feedback anticipatory reasoning