VA-OPD improves VLM performance over standard on-policy distillation by reweighting rollouts and separating KL terms according to token-level visual advantage on math and visual benchmarks.
Visual description grounding reduces hallucinations and boosts reasoning in lvlms
4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
CARE uses exponential moving average competence estimates to progressively shift RL rewards from exploration-oriented long reasoning to efficiency-oriented concise reasoning in video-MLLMs, with batch normalization and posterior amplification, yielding accuracy gains and shorter traces.
Greedy decoding provably outperforms stochastic sampling for VQA under calibration conditions that hold empirically across multiple MLLMs and benchmarks.
A 22M-parameter hyperbolic model answers structured EHR questions with accuracy close to LLM-based systems (EHRXQA 89.5%, MIMIC-Instr 76.0%).
citing papers explorer
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Visual-Advantage On-Policy Distillation for Vision-Language Models
VA-OPD improves VLM performance over standard on-policy distillation by reweighting rollouts and separating KL terms according to token-level visual advantage on math and visual benchmarks.
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CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs
CARE uses exponential moving average competence estimates to progressively shift RL rewards from exploration-oriented long reasoning to efficiency-oriented concise reasoning in video-MLLMs, with batch normalization and posterior amplification, yielding accuracy gains and shorter traces.
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Revisiting Greedy Decoding for Visual Question Answering: A Calibration Perspective
Greedy decoding provably outperforms stochastic sampling for VQA under calibration conditions that hold empirically across multiple MLLMs and benchmarks.
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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
A 22M-parameter hyperbolic model answers structured EHR questions with accuracy close to LLM-based systems (EHRXQA 89.5%, MIMIC-Instr 76.0%).