CoRe²: A Fast and High-Quality Inference Method for Text-to-Image Generation Across Diffusion and Autoregressive Models

I’ve been examining CoRe² (Collect, Reflect, Refine), a new framework that restructures text generation into a three-stage process to optimize both quality and speed. Instead of the standard token-by-token approach or full one-shot generation, CoRe² offers a hybrid solution that significantly improves generation efficiency.

The core methodology works through three distinct stages: – Collect: Generate multiple diverse drafts in parallel using different temperatures and prompting approaches – Reflect: Analyze these drafts to identify strengths, weaknesses, and missing elements – Refine: Generate a final comprehensive response in a single non-autoregressive step using the original prompt, drafts, and reflection

Key technical points and results: – Achieves 2-3x faster generation than standard autoregressive methods while maintaining or improving quality – Outperforms competing approaches like G-Eval and DAG-Search on benchmarks including AlpacaEval 2.0 and MT-Bench – Human evaluators preferred CoRe² responses over standard methods 65% of the time – Works with various LLMs including Claude and GPT models – Requires only a single model instance rather than multiple copies – Ablation studies showed the reflection stage is crucial – removing it substantially reduces performance

I think this approach could be transformative for real-time AI applications where response latency is critical. The speed improvements without quality degradation could make AI assistants feel significantly more responsive and natural in conversation. For enterprise deployments, the framework offers better resource utilization while potentially improving output quality, though the increased token consumption is a consideration for cost-sensitive applications.

The non-autoregressive refinement stage seems particularly promising as a way to bypass the inherent limitations of sequential generation. I think we’ll see this three-stage paradigm adapted to other domains beyond text generation, potentially including code generation and multimodal systems.

TLDR: CoRe² introduces a three-stage framework (collect-reflect-refine) that makes text generation 2-3x faster without sacrificing quality by generating multiple drafts, reflecting on them, then refining them into a final output in one non-autoregressive step.

Full summary is here. Paper here.