#trajectory-optimization News & Analysis

5 articles tagged with #trajectory-optimization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · May 117/10

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Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation

Researchers present Trajectory-Shaped Discrete Flow Matching (TS-DFM), a technique that improves text generation efficiency by using an energy-based guidance system during training to select better token transformation paths. The method enables a compact student model to achieve 32% lower perplexity than a 1,024-step teacher while running 128x faster at just 8 steps, setting new benchmarks for discrete generation tasks.

🏢 Perplexity

AIBullisharXiv – CS AI · Mar 177/10

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Reducing Cost of LLM Agents with Trajectory Reduction

Researchers introduce AgentDiet, a trajectory reduction technique that cuts computational costs for LLM-based agents by 39.9%-59.7% in input tokens and 21.1%-35.9% in total costs while maintaining performance. The approach removes redundant and expired information from agent execution trajectories during inference time.

AIBullisharXiv – CS AI · May 116/10

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WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning

WebClipper is a new framework that optimizes web agent trajectories by pruning redundant reasoning steps through graph-based analysis, reducing tool-call rounds by approximately 20% while maintaining or improving accuracy. The approach models agent search processes as directed acyclic graphs and introduces an F-AE Score metric to measure the balance between accuracy and efficiency in web agent design.

AIBullisharXiv – CS AI · Mar 176/10

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SmoothVLA: Aligning Vision-Language-Action Models with Physical Constraints via Intrinsic Smoothness Optimization

Researchers introduce SmoothVLA, a new reinforcement learning framework that improves robot control by optimizing both task performance and motion smoothness. The system addresses the trade-off between stability and exploration in Vision-Language-Action models, achieving 13.8% better smoothness than standard RL methods.

AINeutralarXiv – CS AI · Mar 54/10

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Q-Guided Stein Variational Model Predictive Control via RL-informed Policy Prior

Researchers have developed Q-SVMPC, a new Model Predictive Control method that combines reinforcement learning with Stein variational inference to improve trajectory optimization. The approach addresses limitations in existing MPC methods that often converge to single solutions, instead maintaining diverse solution paths for better performance in robotics applications.