rDPO uses offline-built rubrics to generate on-policy preference data for DPO, raising benchmark scores in visual tasks over outcome-based filtering and style baselines.
Omni-rrm: Advancing omni reward modeling via automatic rubric-grounded preference synthesis.CoRR, abs/2602.00846
4 Pith papers cite this work. Polarity classification is still indexing.
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citation-polarity summary
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2026 4verdicts
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EarlyTom is a training-free early token compression method inside the vision encoder with decoupled spatial selection that reduces TTFT up to 2.65x and FLOPs 61% on LLaVA-OneVision-7B while keeping accuracy comparable to full tokens.
RLR³ extends RLVR to criterion-level rubric verification via dual execution paths, minimal exposure masking, hierarchical aggregation, and saturation mitigation, delivering 4.7-point gains over base on 15 benchmarks with Qwen3-VL-30B-A3B.
A literature synthesis that organizes post-training reasoning data research around data objects, usefulness factors, construction methods, and scaling behaviors to create an attribution framework.
citing papers explorer
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Visual Preference Optimization with Rubric Rewards
rDPO uses offline-built rubrics to generate on-policy preference data for DPO, raising benchmark scores in visual tasks over outcome-based filtering and style baselines.
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EarlyTom: Early Token Compression Completes Fast Video Understanding
EarlyTom is a training-free early token compression method inside the vision encoder with decoupled spatial selection that reduces TTFT up to 2.65x and FLOPs 61% on LLaVA-OneVision-7B while keeping accuracy comparable to full tokens.
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Reinforcement Learning with Robust Rubric Rewards
RLR³ extends RLVR to criterion-level rubric verification via dual execution paths, minimal exposure masking, hierarchical aggregation, and saturation mitigation, delivering 4.7-point gains over base on 15 benchmarks with Qwen3-VL-30B-A3B.
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A Primer in Post-Training Reasoning Data: What We Know About How It Works
A literature synthesis that organizes post-training reasoning data research around data objects, usefulness factors, construction methods, and scaling behaviors to create an attribution framework.