4 articles tagged with #test-time-optimization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose QGF (Q-Guided Flow), a reinforcement learning algorithm that optimizes policies entirely at test time using value gradients to guide pre-trained flow models, avoiding the training instability issues of traditional actor-critic approaches while maintaining competitive performance on offline RL benchmarks.
Researchers propose AISP (Adaptive Importance Sampling on Pre-logits), a test-time alignment method for large language models that uses Gaussian perturbations to optimize reward signals without expensive fine-tuning. The technique outperforms existing sampling-based approaches and represents progress in making LLM alignment more computationally efficient.
Researchers introduce ECHO, a novel test-time reinforcement learning algorithm that addresses rollout collapse and noisy pseudo-labels through entropy-confidence hybrid optimization. The method improves sampling efficiency and training robustness across mathematical and visual reasoning benchmarks while performing better under limited computational budgets.
Researchers introduce TTOM (Test-Time Optimization and Memorization), a training-free framework that improves compositional video generation in Video Foundation Models during inference. The system uses layout-attention optimization and parametric memory to better align text prompts with generated video outputs, showing strong transferability across different scenarios.
