PILOT: privileged training-time reasoning exploration boosts multimodal embeddings
_reachsumit · x · 2026-10-26
The arXiv paper "Beyond Post-Hoc Rewards: Privileged Reasoning Exploration for Multimodal Embeddings" introduces PILOT, which treats generated reasoning as a latent variable to improve reasoning-augmented multimodal embeddings.
- Problem: conventional pairwise retrieval supervision never explicitly guides the self-generated reasoning used to augment inputs; existing methods optimize reasoning conditioned on the input alone.
- Method: a shared MLLM instantiates prior (input-only) and posterior (training-time access to relational evidence from positive/negative candidates) reasoning modes, trained via an importance-weighted variational objective under a shared inference-compatible reward.
- Result: richer reasoning exploration at training time translates into better embeddings at inference, where only the input is needed.
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