Search-R3: Unifying Reasoning and Embedding Generation in Large Language
  Models
Despite their remarkable natural language understanding capabilities, Large
Language Models (LLMs) have been underutilized for retrieval tasks. We present
Search-R3, a novel framework that addresses this limitation by adapting LLMs to
generate search embeddings as a direct output of their reasoning process. Our
approach exploits LLMs' chain-of-thought capabilities, allowing them to produce
more effective embeddings by reasoning step-by-step through complex semantic
analyses. We implement this through three complementary mechanisms. (1) a
supervised learning stage enables the model's ability to produce quality
embeddings, (2) a reinforcement learning (RL) methodology that optimizes
embedding generation alongside reasoning, and (3) a specialized RL environment
that efficiently handles evolving embedding representations without requiring
complete corpus re-encoding at each training iteration. Our extensive
evaluations on diverse benchmarks demonstrate that Search-R3 significantly
outperforms prior methods by unifying the reasoning and embedding generation
processes. This integrated post-training approach represents a substantial
advancement in handling complex knowledge-intensive tasks that require both
sophisticated reasoning and effective information retrieval. Project page:
https://github.com/ytgui/Search-R3
68e7bde844180c42eca8b034
Yuntao Gui
claimed_verified
2025-10-10T09:12:55.602Z
66025038c618afa8d799261f
/avatars/ac914b733a1b3e5763a3e7d55bf9f108.svg
Yuntao
user
ytgui
68e7bde844180c42eca8b035
James Cheng
Search-R3: Unifying Reasoning and Embedding Generation in Large Language
  Models
Despite their remarkable natural language understanding capabilities, Large
Language Models (LLMs) have been underutilized for retrieval tasks. We present
Search-R3, a novel framework that addresses this limitation by adapting LLMs to
generate search embeddings as a direct output of their reasoning process. Our
approach exploits LLMs' chain-of-thought capabilities, allowing them to produce
more effective embeddings by reasoning step-by-step through complex semantic
analyses. We implement this through three complementary mechanisms. (1) a
supervised learning stage enables the model's ability to produce quality
embeddings, (2) a reinforcement learning (RL) methodology that optimizes
embedding generation alongside reasoning, and (3) a specialized RL environment
that efficiently handles evolving embedding representations without requiring
complete corpus re-encoding at each training iteration. Our extensive
evaluations on diverse benchmarks demonstrate that Search-R3 significantly
outperforms prior methods by unifying the reasoning and embedding generation
processes. This integrated post-training approach represents a substantial
advancement in handling complex knowledge-intensive tasks that require both
sophisticated reasoning and effective information retrieval. Project page:
https://github.com/ytgui/Search-R3
Search-R3 is a framework that adapts LLMs to generate effective search embeddings through chain-of-thought reasoning, supervised learning, and reinforcement learning.
Large Language Models (LLMs) search embeddings chain-of-thought capabilities supervised learning reinforcement learning (RL) RL environment embedding representations reasoning information retrieval