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Guidance and alignment for flow matching

Improve guidance, stability, and reward alignment in flow matching models.

34 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2025–2026) · an approach in Fast diffusion sampling, keyword cluster “flow · matching · rectified”

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Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

0 papers in 2023–24 and 34 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 0.0% to 2.6%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 0162017: 0172018: 0182019: 0192020: 0202021: 0212022: 0222023: 0232024: 0242025: 8252026: 2626

Most cited paper of each year

Earliest papers here

What has been tried

7 papers here report 79 measured changes (a component added, removed or swapped, compared with a baseline): helped 70%, no clear effect 6%, hurt 24%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
add a distance score map to guide rectified flowNavsim Test · SPDM85.6 -> 88.5HelpedGoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving (CVPR 2025 · 30 citations)
add dynamic radius schedulingRLBench multi-task · average success rate79.1 -> 82.3HelpedFlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation (CVPR 2025 · 4 citations)
Increase guidance strength from γ=20 to γ=200Class 2 · Prob0.313 ± 0.343 -> 0.951 ± 0.110HelpedTraining-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models (NeurIPS 2025 · 4 citations)
optimize translations only instead of translations and rotationsPeptide design test set · IMP (%)15.0 -> 14.4No clear effectTraining Free Guided Flow-Matching with Optimal Control (ICLR 2025 · 1 citation)
Use a higher reward temperature βAesthetic Score · reward convergence speedHelpedValue Gradient Guidance for Flow Matching Alignment (NeurIPS 2025 · 0 citations)
Add predictor to the methodMS-COCO 1K · FID73.6981 -> 72.9745HelpedRectified CFG++ for Flow Based Models (NeurIPS 2025 · 0 citations)

What reviewers will expect

Datasets and baselines named in the 14 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • CelebA-HQ · 2 of 14 papers

Recent preprints

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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