CRINN: Contrastive Reinforcement Learning for Approximate Nearest
  Neighbor Search
Authors: name: Xiaoya Li, name: Xiaofei Sun, name: Albert Wang, name: Chris Shum, name: Jiwei Li

Approximate nearest-neighbor search (ANNS) algorithms have become
increasingly critical for recent AI applications, particularly in
retrieval-augmented generation (RAG) and agent-based LLM applications. In this
paper, we present CRINN, a new paradigm for ANNS algorithms. CRINN treats ANNS
optimization as a reinforcement learning problem where execution speed serves
as the reward signal. This approach enables the automatic generation of
progressively faster ANNS implementations while maintaining accuracy
constraints. Our experimental evaluation demonstrates CRINN's effectiveness
across six widely-used NNS benchmark datasets. When compared against
state-of-the-art open-source ANNS algorithms, CRINN achieves best performance
on three of them (GIST-960-Euclidean, MNIST-784-Euclidean, and
GloVe-25-angular), and tied for first place on two of them (SIFT-128-Euclidean
and GloVe-25-angular). The implications of CRINN's success reach well beyond
ANNS optimization: It validates that LLMs augmented with reinforcement learning
can function as an effective tool for automating sophisticated algorithmic
optimizations that demand specialized knowledge and labor-intensive manual
refinement.Code can be found at https://github.com/deepreinforce-ai/CRINN