RGLT: Retrieval Grounded Latent Reasoning Improves Dense Retrieval Accuracy
_reachsumit · x · 2026-08-17
This paper presents Retrieval Grounded Latent Reasoning (RGLT), a framework for dense retrieval that grounds latent reasoning steps in actual retrieval gains. Unlike existing methods dominated by final objectives, RGLT constructs instruction-conditioned trajectories using silent tokens, explicitly linking latent states to retrieval improvements. It combines process-supervised distillation with retrieval-grounded supervision to optimize incremental gains. Experiments show RGLT outperforms strong baselines on reasoning-intensive benchmarks while maintaining efficient inference.
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