Self-Augmenting Retrieval for Diffusion Language Models
Why This Matters
Highly relevant to LLM integration, RAG, and fine-tuning through the proposal of SARDI, a dynamic RAG framework that uses lookahead tokens to guide retrieval during denoising. The paper presents empirical results, showing that SARDI outperforms current training-free diffusion and autoregressive retrieval baselines.
Abstract
Discrete diffusion language models generate text by iteratively denoising an entire response in parallel. At each step, they predict tentative tokens for every masked position, committing the confident predictions to the output and discarding the unconfident ones. We show that the discarded tokens are in fact a useful lookahead signal for retrieval-augmented generation: even low-confidence tokens often surface salient entities early in the denoising trajectory, enabling retrieval of stronger evidence before the output is finalized. We exploit this through Self-Augmenting Retrieval for Diffusion Language Models (SARDI), a dynamic RAG framework that uses these lookahead tokens to guide retrieval during denoising. SARDI is training-free, retriever-agnostic, and applicable to any reasoning-capable discrete diffusion language model. Across five multi-hop QA benchmarks, SARDI outperforms current training-free diffusion and autoregressive retrieval baselines at up to $8\times$ higher throughput.
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