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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.

4 Pith papers citing it

citation-role summary

background 1

citation-polarity summary

fields

cs.CV 3 cs.CL 1

years

2026 4

verdicts

UNVERDICTED 4

roles

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background 1

representative citing papers

Visual Preference Optimization with Rubric Rewards

cs.CV · 2026-04-14 · unverdicted · novelty 7.0

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.

EarlyTom: Early Token Compression Completes Fast Video Understanding

cs.CV · 2026-05-28 · unverdicted · novelty 6.0

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.

Reinforcement Learning with Robust Rubric Rewards

cs.CV · 2026-05-28 · unverdicted · novelty 5.0

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.

citing papers explorer

Showing 4 of 4 citing papers.

  • Visual Preference Optimization with Rubric Rewards cs.CV · 2026-04-14 · unverdicted · none · ref 40 · internal anchor

    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.

  • EarlyTom: Early Token Compression Completes Fast Video Understanding cs.CV · 2026-05-28 · unverdicted · none · ref 17 · internal anchor

    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.

  • Reinforcement Learning with Robust Rubric Rewards cs.CV · 2026-05-28 · unverdicted · none · ref 26 · internal anchor

    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 Primer in Post-Training Reasoning Data: What We Know About How It Works cs.CL · 2026-06-01 · unverdicted · none · ref 8 · internal anchor

    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.