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continual learning 23 reinforcement learning 9 language models 6 benchmarking 4 natural language processing 4 AI agents 3 vision-language models 3 world models 3

30 JUL 2026 · Paper

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms

This paper compares the performance of different retrieval-augmented generation (RAG) paradigms at varying corpus sizes, finding that BM25 outperforms others at larger scales, but not at smaller ones, and that lexical retrieval is the strongest scalable default.

RAG lexical retrieval BM25 scalability corpus size

29 JUL 2026 · Paper

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

This paper introduces Explorative Modeling, a new approach to training generative models that allows for end-to-end generation by exploring multiple candidate matches between model generations and data. This can lead to improved performance and efficiency in various applications.

generative models end-to-end training mode handling pretraining continual learning scalability