Curiosity-Driven Development of Action and Language in Robots Through Self-Exploration

Auto3R has been introduced as a data-driven model aimed at automating the 3D scanning and reconstruction of scenes and objects, including those with complex materials. This model utilizes uncertainty quantification to predict optimal scanning viewpoints without prior knowledge of the actual geometry or appearance, significantly enhancing the efficiency and accuracy of the process.

The rapid advancement of artificial intelligence is facilitating the development of autonomous robots capable of functioning in unstructured human environments. This evolution presents a significant autonomy-alignment problem, focusing on how to ensure that robots' autonomous learning aligns with human values and practical objectives, particularly in the context of open-ended learning robots that acquire knowledge through environmental interaction.

Recent research has introduced a novel approach to tokenizer adaptation for pre-trained language models, focusing on vocabulary extension and pruning. The method, termed continued BPE training, enhances tokenization efficiency by continuing the BPE merge learning process on new data, while leaf-based vocabulary pruning removes redundant tokens without compromising model quality.

A recent study on arXiv presents a novel approach to LLM routing, focusing on minimizing decision-making regret through observational data rather than relying on full-feedback data. This end-to-end framework aims to enhance the selection process of language models for queries by balancing accuracy and cost effectively.

DeepSeek-OCR has demonstrated that rendered text can be reconstructed with high fidelity from a limited number of vision tokens, raising interest in vision-based context compression for language models. However, the evaluation of these representations' effectiveness in language modeling remains untested, leading to skepticism about their practical utility.