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ARXIV:2604.13993 · VISION-LANGUAGE MODELS · SUBMITTED 16 APR · 18:19 UTC · FRESHNESS STALE
ARXIV:2604.13993VISION-LANGUAGE MODELSSUBMITTED 16 APR · 18:19 UTCFRESHNESS STALEDerek Lilienthal · Manisha Mukherjee · Sameera Horawalavithana · arXiv
This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals.
Opportunity summary
Pain This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on…
Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physics benchmarks.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Reward design does not uniformly improve performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals.
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10.48550/arXiv.2604.13993This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals.
Abstract
Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physics benchmarks. While post-training algorithms such as Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) have demonstrated strong reasoning gains in language models, how reward design shapes VLM physical reasoning behavior remains poorly understood. We present a systematic reward ablation study for GRPO-based VLM training on physical reasoning. We compare four reward signals of increasing semantic richness: format compliance, answer accuracy, a composite rubric reward (answer correctness, physics principle identification, and unit consistency), and a novel internal reward derived from model attention weights over input image regions. We evaluate on PhyX, a 3,000-problem benchmark spanning six physics domains and six reasoning types across multiple-choice and open-ended formats, using IBM Granite Vision 3.3 (2B). Across both formats, GRPO with accuracy-based rewards outperforms SFT on most domains, though gains vary substantially by reward type and domain. Reward design does not uniformly improve performance. Instead, it induces domain-specific reasoning behaviors. Accuracy-based rewards provide the strongest overall gains. Rubric rewards improve structured reasoning quality without consistent accuracy improvements. Attention-based rewards enhance spatial reasoning while degrading performance in symbolic domains. Our internal attention-weight reward requires no spatial annotations and improves spatial relation accuracy from 0.27 to 0.50, suggesting that supervising where the model attends during generation is a promising direction for visually grounded physical reasoning.
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PROBLEM
This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physic...
METHOD
Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physics benchmarks.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Reward design does not uniformly improve performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physics benchmarks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even state-of-the-art Vision Language Models (VLMs) fall far short of human performance on physics benchmarks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Reward design does not uniformly improve performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Models moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research systematically investigates reward design for improving physical reasoning in vision-language models, demonstrating accuracy gains through targeted reward signals.
Segment
Vision-Language Models
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