How does the inference efficiency (tokens/sec and memory usage) of a 70B-parameter LLM agent compare when usin - Assignee Research (Assignee Research, 2026)
Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval approaches have emerged, typically involving the fine-tuning of small LLMs to perform multi-step retrieval. This type of fine-tuning is highly re
Machine-generated literature synthesis.