SGCD generates supervision for off-trajectory states in GUI agents by mixing expert trajectories with continuations produced by a skill-guided policy after the base policy reaches those states.
Efficient multi-turn rl for gui agents via decoupled training and adaptive data curation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.AI 3years
2026 3verdicts
UNVERDICTED 3roles
background 2representative citing papers
ToolCUA introduces a trajectory scaling pipeline and staged RL to optimize GUI-tool switching, reaching 46.85% accuracy on OSWorld-MCP for a 66% relative gain over baseline.
An exploration-aware policy optimization method lets LLM agents explore selectively via a variational-inference reward and action grouping, yielding consistent gains on text and GUI agent benchmarks.
citing papers explorer
-
Skill-Guided Continuation Distillation for GUI Agents
SGCD generates supervision for off-trajectory states in GUI agents by mixing expert trajectories with continuations produced by a skill-guided policy after the base policy reaches those states.
-
ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents
ToolCUA introduces a trajectory scaling pipeline and staged RL to optimize GUI-tool switching, reaching 46.85% accuracy on OSWorld-MCP for a 66% relative gain over baseline.
-
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
An exploration-aware policy optimization method lets LLM agents explore selectively via a variational-inference reward and action grouping, yielding consistent gains on text and GUI agent benchmarks.