Sample Efficiency

Why this matters now Reinforcement Learning from Human Feedback (RLHF) has become the de facto standard for aligning large language models with human values. Yet, the process remains painfully inefficient—annotators evaluate thousands of pairs, most of which offer little new information. As AI models scale, so does the human cost. The question is no longer can we align models, but can we afford to keep doing it this way? A recent paper from Politecnico di Milano proposes a pragmatic answer: inject Bayesian intelligence into the feedback loop. Their hybrid framework—Bayesian RLHF—blends the scalability of neural reinforcement learning with the data thriftiness of Bayesian optimization. The result: smarter questions, faster convergence, and fewer wasted clicks. ...

Training AI agents to navigate complex environments has always faced a fundamental bottleneck: the overwhelming number of possible actions. Traditional reinforcement learning (RL) techniques often suffer from inefficient exploration, especially in sparse-reward or high-dimensional settings. Recent research offers a promising breakthrough. By leveraging Vision-Language Models (VLMs) and structured generation pipelines, agents can now automatically discover affordances—context-specific action possibilities—without exhaustive trial-and-error. This new paradigm enables AI to focus only on relevant actions, dramatically improving sample efficiency and learning speed. ...