Do Reasoning Models Enhance Embedding Models?
State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? Contrary to expectation, our evaluation on MTEB and BRIGHT reveals a **null effect**: embedding models initialized from RLVR-tuned backbones yield no consistent performance advantage over their base counterparts when subjected to identical training recipes. To unpack this paradox, we introduce **H**ierarchical **R**epresentation **S**imilarity **A**nalysis (HRSA), a framework that decomposes similarity across representation, geometry, and function levels. HRSA reveals that while RLVR induces irreversible latent manifold's local geometry reorganization and reversible coordinate basis drift, it preserves the global manifold geometry and linear readout. Consequently, subsequent contrastive learning drives strong alignment between base- and reasoning-initialized models, a phenomenon we term **Manifold Realignment**. Empirically, our findings suggest that unlike Supervised Fine-Tuning (SFT), RLVR optimizes trajectories within an existing semantic landscape rather than fundamentally restructuring the landscape itself.
697cae786676f933227060de
Wun Yu Chan
claimed_verified
2026-02-02T16:58:57.137Z
66a36281e5b14ef0e6d3befa
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/gejfR0L2VGYWBvVfHTyC6.png
Lucas Chan
lucaswychan
user
lucaswychan
697cae786676f933227060df
Shaojin Chen
697cae786676f933227060e0
Huihao Jing
697cae786676f933227060e1
Kwun Hang Lau
697cae786676f933227060e2
Elton Chun-Chai Li
697cae786676f933227060e3
Zihao Wang
697cae786676f933227060e4
Haoran Li
697cae786676f933227060e5
Yangqiu Song
Do Reasoning Models Enhance Embedding Models?
State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? Contrary to expectation, our evaluation on MTEB and BRIGHT reveals a **null effect**: embedding models initialized from RLVR-tuned backbones yield no consistent performance advantage over their base counterparts when subjected to identical training recipes. To unpack this paradox, we introduce **H**ierarchical **R**epresentation **S**imilarity **A**nalysis (HRSA), a framework that decomposes similarity across representation, geometry, and function levels. HRSA reveals that while RLVR induces irreversible latent manifold's local geometry reorganization and reversible coordinate basis drift, it preserves the global manifold geometry and linear readout. Consequently, subsequent contrastive learning drives strong alignment between base- and reasoning-initialized models, a phenomenon we term **Manifold Realignment**. Empirically, our findings suggest that unlike Supervised Fine-Tuning (SFT), RLVR optimizes trajectories within an existing semantic landscape rather than fundamentally restructuring the landscape itself.
Embedding models initialized from RLVR-tuned reasoning models show no performance advantage over base models, with HRSA revealing preserved global geometry and linear readout despite local geometric reorganization.
embedding models decoder-only LLM contrastive learning Reinforcement Learning with Verifiable Rewards MTEB BRIGHT hierarchical representation similarity analysis latent manifold global manifold geometry local geometry reorganization coordinate basis drift Manifold Realignment supervised fine-tuning