These have the opposite properties, with DRL having good sample efficiencyandpoor stability, while ESbeing vice versa. Recently,there havebeen attempts tocombine these algorithms, butthesemethods fullyrelyonsynchronous updatescheme, making it not ideal to maximize the benefits of the parallelism in ES.

Decision trees are widely used in many settings where interpretable models are preferred orrequired.

Pre-training large language models (PrLMs) on massive unlabeled corpora and fine-tuning them on downstream tasks has become a new paradigm [1-3]. Their success can be partly attributed to the self-attention mechanism [4], yet these self-attention networks are often redundant [5, 6] and tend to cause overfitting when fine-tuned on downstream tasks due to the mismatch between their overparameterization and the limited annotated data [7-13]. To address this issue, various regularization techniques such as data augmentation [14, 15], adversarial training [16, 17]), and dropout-based methods [11,13,18]have been developed.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Figure 5: Left: Visualizationofthe Motorcyclesimulator. Right: Performanceacross 10 runs 1 standarddeviation.

Thus, there is a need for a reference point, on which each model canbetested andfrom where potential improvements canbe derived. In this study, we select publicly available state-of-the-art visual search models and datasets in natural scenes, and provide a common framework for their evaluation. To this end, we apply a unified format and criteria, bridging the gaps between them, and we estimate the models' efficiency and similarity with humans using a specific set of metrics.