Papers with iterative refinement process
Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)
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| Challenge: | Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data. |
| Approach: | They propose a framework that facilitates information refinement through synergy between RMs and LLMs. |
| Outcome: | The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks. |
Deep Equilibrium Non-Autoregressive Sequence Learning (2023.findings-acl)
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| Challenge: | et al., 2017) is the most prevailing neural architecture for sequence-to-sequence learning. |
| Approach: | They propose to solve for the equilibrium state of NAR models with black-box root-finding solvers and back-propagate through the equilibrium point via implicit differentiation with constant memory. |
| Outcome: | The proposed framework can converge to a more accurate prediction on four WMT benchmarks. |
Scaling Unverifiable Rewards: A Case Study on Visual Insights (2026.findings-acl)
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| Challenge: | Existing methods to scale complex, open-ended tasks with unverifiable rewards are not scalable to multi-stage pipelines. |
| Approach: | They propose a process-based refinement framework that scales inference across stages of a multi-agent pipeline, instead of refining a single output over time. |
| Outcome: | The proposed framework scales inference across stages of a multi-agent pipeline, instead of refining a single output over time as in prior work. |
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier (2026.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enhanced capabilities in complex reasoning through step-by-step trace generation. |
| Approach: | They propose a generative verifier that dynamically allocates compute between rapid fast thinking and deliberative slow thinking. |
| Outcome: | The proposed solution outperforms GenPRM-32B on ProcessBench while requiring 2.3x fewer TFLOPS and 15x less training data. |