GRACE: Generative Representation Learning via Contrastive Policy
  Optimization
Authors: name: Jiashuo Sun, name: Shixuan Liu, name: Zhaochen Su, name: Xianrui Zhong, name: Pengcheng Jiang, name: Bowen Jin, name: Peiran Li, name: Weijia Shi, name: Jiawei Han
Organization: University of Illinois at Urbana-Champaign

Prevailing methods for training Large Language Models (LLMs) as text encoders
rely on contrastive losses that treat the model as a black box function,
discarding its generative and reasoning capabilities in favor of static
embeddings. We introduce GRACE (Generative Representation Learning via
Contrastive Policy Optimization), a novel framework that reimagines contrastive
signals not as losses to be minimized, but as rewards that guide a generative
policy. In GRACE, the LLM acts as a policy that produces explicit,
human-interpretable rationales--structured natural language explanations of its
semantic understanding. These rationales are then encoded into high-quality
embeddings via mean pooling. Using policy gradient optimization, we train the
model with a multi-component reward function that maximizes similarity between
query positive pairs and minimizes similarity with negatives. This transforms
the LLM from an opaque encoder into an interpretable agent whose reasoning
process is transparent and inspectable. On MTEB benchmark, GRACE yields broad
cross category gains: averaged over four backbones, the supervised setting
improves overall score by 11.5% over base models, and the unsupervised variant
adds 6.9%, while preserving general capabilities. This work treats contrastive
objectives as rewards over rationales, unifying representation learning with
generation to produce stronger embeddings and transparent rationales. The
model, data and code are available at https://github.com/GasolSun36/GRACE.