--- title: Understanding Alignment in Multimodal LLMs: A... description: Apple ML Research presents a systematic study of preference alignment in Multimodal Large Language Models (MLLMs). The paper categorizes alignment algorithms... canonical: https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Understanding Alignment in Multimodal LLMs: A Comprehensive Study | daily.dev og:description: Apple ML Research presents a systematic study of preference alignment in Multimodal Large Language Models (MLLMs). The paper categorizes alignment algorithms... og:url: https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw og:image: https://api.daily.dev/og/posts/8e9C0MQGw.png og:image:alt: Understanding Alignment in Multimodal LLMs: A Comprehensive Study og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Understanding Alignment in Multimodal LLMs: A Comprehensive Study **[Apple Machine Learning Research](https://daily.dev/sources/mlapple)** · 2 min read · 1 upvotes · 0 comments ## Summary Apple ML Research presents a systematic study of preference alignment in Multimodal Large Language Models (MLLMs). The paper categorizes alignment algorithms into offline (e.g., DPO) and online (e.g., online-DPO) methods, finding that combining both can improve performance. It reviews existing multimodal preference datasets and their construction impact, then introduces Bias-Driven Hallucination Sampling (BDHS), a novel method for generating multimodal preference data without additional annotation or external models. BDHS achieves competitive results across multiple benchmarks compared to prior alignment approaches. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is Bias-Driven Hallucination Sampling (BDHS) for multimodal LLM alignment? Bias-Driven Hallucination Sampling is a method for constructing multimodal preference data without requiring additional human annotation or external models. It works by exploiting known model biases to sample hallucinated responses, creating preference pairs that can be used for alignment training. It achieves competitive performance compared to previously published alignment approaches across a range of benchmarks, while being cheaper to produce. _Teams comparing alignment data pipelines for multimodal models can follow method breakdowns like this on daily.dev._ ### Does combining offline and online preference alignment methods improve multimodal LLM performance? Yes, combining offline methods like DPO with online methods like online-DPO can improve multimodal LLM performance in certain scenarios. Offline and online alignment algorithms were analyzed independently and found to have complementary strengths, so blending them helps address image-response inconsistency and hallucination issues better than either approach alone. _Anyone deciding between offline and online alignment strategies can track findings like these on daily.dev._ ## Similar posts on daily.dev - [How LLMs Learn to Be Helpful \(RLHF vs DPO\)](https://daily.dev/posts/how-llms-learn-to-be-helpful-rlhf-vs-dpo--rxctuhdqz) · ByteByteGo · 5 upvotes · 0 comments - [LLM Fine-tuning: Techniques for Adapting Language Models](https://daily.dev/posts/llm-fine-tuning-techniques-for-adapting-language-models-becy4meiz) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm) [View this post on daily.dev](https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Understanding Alignment in Multimodal LLMs: A Comprehensive Study","url":"https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw"},"datePublished":"2026-08-03T15:37:31.723Z","dateModified":"2026-09-14T06:53:55.787Z","description":"Apple ML Research presents a systematic study of preference alignment in Multimodal Large Language Models (MLLMs). The paper categorizes alignment algorithms...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/8dda20dea5f9a8a0e25a1b7bb36a1a20?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/8dda20dea5f9a8a0e25a1b7bb36a1a20?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Apple Machine Learning Research","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Apple Machine Learning Research","logo":"https://media.daily.dev/image/upload/s--mD-1uSe0--/f_auto,q_auto/v1785654191/logos/mlapple","url":"https://daily.dev/sources/mlapple"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,llm","timeRequired":"PT2M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Apple Machine Learning Research","item":"https://daily.dev/sources/mlapple"},{"@type":"ListItem","position":3,"name":"Understanding Alignment in Multimodal LLMs: A Comprehensive Study"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/understanding-alignment-in-multimodal-llms-a-comprehensive-study-8e9c0mqgw#faq","mainEntity":[{"@type":"Question","name":"What is Bias-Driven Hallucination Sampling (BDHS) for multimodal LLM alignment?","acceptedAnswer":{"@type":"Answer","text":"Bias-Driven Hallucination Sampling is a method for constructing multimodal preference data without requiring additional human annotation or external models. It works by exploiting known model biases to sample hallucinated responses, creating preference pairs that can be used for alignment training. It achieves competitive performance compared to previously published alignment approaches across a range of benchmarks, while being cheaper to produce. Teams comparing alignment data pipelines for multimodal models can follow method breakdowns like this on daily.dev."}},{"@type":"Question","name":"Does combining offline and online preference alignment methods improve multimodal LLM performance?","acceptedAnswer":{"@type":"Answer","text":"Yes, combining offline methods like DPO with online methods like online-DPO can improve multimodal LLM performance in certain scenarios. Offline and online alignment algorithms were analyzed independently and found to have complementary strengths, so blending them helps address image-response inconsistency and hallucination issues better than either approach alone. Anyone deciding between offline and online alignment strategies can track findings like these on daily.dev."}}]} ```