Improving Large Vision and Language Models by Learning from a Panel of
  Peers
Traditional alignment methods for Large Vision and Language Models (LVLMs)
primarily rely on human-curated preference data. Human-generated preference
data is costly; machine-generated preference data is limited in quality; and
self-supervised preference data often introduces hallucinations. To overcome
these limitations, we propose a novel Panel-of-Peers learning framework
inspired by collaborative learning among humans. This approach leverages a
panel of LVLMs, each evaluating and learning from their collective outputs
through an iterative self-improvement process. By simulating a peer review
system, our models generate, assess, and refine outputs in response to a
curated set of prompts, mimicking a classroom learning environment. We
demonstrate that this methodology enhances model performance without requiring
extensive human-labeled datasets. Our experiments show significant improvement
across multiple benchmarks, demonstrating the potential of peer evaluations as
a scalable alternative to self-supervised alignment. Notably, we show that
Panel-of-Peers increases the average score on fifteen benchmarks from 48% to
57%
68b7be87295f15ff6091137d
Jefferson Hernandez
68b7be87295f15ff6091137e
Jing Shi
68b7be87295f15ff6091137f
Simon Jenni
68b7be87295f15ff60911380
Vicente Ordonez
admin_assigned
2025-09-03T09:02:04.432Z
636768ee4e7a848a18855058
https://cdn-avatars.huggingface.co/v1/production/uploads/636768ee4e7a848a18855058/2XQs2bQfvVa4l3_qkyoJU.jpeg
Vicente Ordonez
user
vicenteor
68b7be87295f15ff60911381
Kushal Kafle
admin_assigned
2025-09-03T09:01:59.077Z
66d5e56cd79c95244f0ec3ea
/avatars/589f0bfe8841846cb1233894204f8ef3.svg
Kushal Kafle
user
kushalkafle
Improving Large Vision and Language Models by Learning from a Panel of
  Peers
Traditional alignment methods for Large Vision and Language Models (LVLMs)
primarily rely on human-curated preference data. Human-generated preference
data is costly; machine-generated preference data is limited in quality; and
self-supervised preference data often introduces hallucinations. To overcome
these limitations, we propose a novel Panel-of-Peers learning framework
inspired by collaborative learning among humans. This approach leverages a
panel of LVLMs, each evaluating and learning from their collective outputs
through an iterative self-improvement process. By simulating a peer review
system, our models generate, assess, and refine outputs in response to a
curated set of prompts, mimicking a classroom learning environment. We
demonstrate that this methodology enhances model performance without requiring
extensive human-labeled datasets. Our experiments show significant improvement
across multiple benchmarks, demonstrating the potential of peer evaluations as
a scalable alternative to self-supervised alignment. Notably, we show that
Panel-of-Peers increases the average score on fifteen benchmarks from 48% to
57%
A Panel-of-Peers learning framework enhances Large Vision and Language Models by simulating peer reviews, improving performance without extensive human-labeled datasets.
Large Vision and Language Models LVLMs Panel-of-Peers collaborative learning peer review iterative self-improvement benchmarks