{"id":80517,"date":"2026-04-01T14:49:05","date_gmt":"2026-04-01T14:49:05","guid":{"rendered":"https:\/\/youzum.net\/liquid-ai-released-lfm2-5-350m-a-compact-350m-parameter-model-trained-on-28t-tokens-with-scaled-reinforcement-learning\/"},"modified":"2026-04-01T14:49:05","modified_gmt":"2026-04-01T14:49:05","slug":"liquid-ai-released-lfm2-5-350m-a-compact-350m-parameter-model-trained-on-28t-tokens-with-scaled-reinforcement-learning","status":"publish","type":"post","link":"https:\/\/youzum.net\/th\/liquid-ai-released-lfm2-5-350m-a-compact-350m-parameter-model-trained-on-28t-tokens-with-scaled-reinforcement-learning\/","title":{"rendered":"Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning"},"content":{"rendered":"<p>In the current landscape of generative AI, the \u2018scaling laws\u2019 have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention with the release of <strong>LFM2.5-350M<\/strong>. This model is actually a technical case study in intelligence density with additional pre-training (from 10T to 28T tokens) and large-scale reinforcement learning<\/p>\n<p>The significance of LFM2.5-350M lies in its architecture and training efficiency. While the most AI companies has been focused on frontier models, Liquid AI is targeting the \u2018edge\u2019\u2014devices with limited memory and compute\u2014by proving that a 350-million parameter model can outperform <strong>models more than twice its size on several evaluated benchmarks<\/strong>.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"2400\" height=\"1800\" data-attachment-id=\"78728\" data-permalink=\"https:\/\/www.marktechpost.com\/2026\/03\/31\/liquid-ai-released-lfm2-5-350m-a-compact-350m-parameter-model-trained-on-28t-tokens-with-scaled-reinforcement-learning\/69ca8b787a45a5bfb5a222b8_lfm2-5-350m-benchmarks-2\/\" data-orig-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/69ca8b787a45a5bfb5a222b8_LFM2.5-350M-Benchmarks-1.png\" data-orig-size=\"2400,1800\" data-comments-opened=\"1\" data-image-meta='{\"aperture\":\"0\",\"credit\":\"\",\"camera\":\"\",\"caption\":\"\",\"created_timestamp\":\"0\",\"copyright\":\"\",\"focal_length\":\"0\",\"iso\":\"0\",\"shutter_speed\":\"0\",\"title\":\"\",\"orientation\":\"0\"}' data-image-title=\"69ca8b787a45a5bfb5a222b8_LFM2.5-350M &#8211; Benchmarks\" data-image-description=\"\" data-image-caption=\"\" data-medium-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/69ca8b787a45a5bfb5a222b8_LFM2.5-350M-Benchmarks-1-300x225.png\" data-large-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/69ca8b787a45a5bfb5a222b8_LFM2.5-350M-Benchmarks-1-1024x768.png\" src=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/69ca8b787a45a5bfb5a222b8_LFM2.5-350M-Benchmarks-1.png\" alt=\"\" class=\"wp-image-78728\" \/><figcaption class=\"wp-element-caption\">https:\/\/www.liquid.ai\/blog\/lfm2-5-350m-no-size-left-behind<\/figcaption><\/figure>\n<\/div>\n<h3 class=\"wp-block-heading\"><strong>Architecture: The Hybrid LIV Backbone<\/strong><\/h3>\n<p>The core technical differentiator of the LFM2.5-350M is its departure from the pure Transformer architecture. It utilizes a hybrid structure built on <strong>Linear Input-Varying Systems (LIVs)<\/strong>.<\/p>\n<p>Traditional Transformers rely entirely on self-attention mechanisms, which suffer from quadratic scaling issues: as the context window grows, the memory and computational requirements for the Key-Value (KV) cache increase. <strong>Liquid AI addresses this by using a hybrid backbone consisting of:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>10 Double-Gated LIV Convolution Blocks:<\/strong> These handle the majority of the sequence processing. LIVs function similarly to advanced Recurrent Neural Networks (RNNs) but are designed to be more parallelizable and stable during training. They maintain a constant-state memory, reducing the I\/O overhead.<\/li>\n<li><strong>6 Grouped Query Attention (GQA) Blocks:<\/strong> By integrating a small number of attention blocks, the model retains high-precision retrieval and long-range context handling without the full memory overhead of a standard Transformer.<\/li>\n<\/ul>\n<p>This hybrid approach allows the LFM2.5-350M to support a <strong>32k context window<\/strong> (32,768 tokens) while maintaining an extremely lean memory footprint.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Performance and Intelligence Density<\/strong><\/h3>\n<p>The LFM2.5-350M was pre-trained on <strong>28 trillion tokens<\/strong> with an extremely high training-to-parameter ratio. This ensures that the model\u2019s limited parameter count is utilized to its maximum potential, resulting in high \u2018intelligence density.\u2019<\/p>\n<h4 class=\"wp-block-heading\"><strong>Benchmarks and Use Cases<\/strong><\/h4>\n<p>The LFM2.5-350M is a specialist model designed for high-speed, agentic tasks rather than general-purpose reasoning.<\/p>\n<figure class=\"wp-block-table is-style-stripes\">\n<table class=\"has-fixed-layout\">\n<thead>\n<tr>\n<td><strong>Benchmark<\/strong><\/td>\n<td><strong>Score<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>IFEval (Instruction Following)<\/strong><\/td>\n<td>76.96<\/td>\n<\/tr>\n<tr>\n<td><strong>GPQA Diamond<\/strong><\/td>\n<td>30.64<\/td>\n<\/tr>\n<tr>\n<td><strong>MMLU-Pro<\/strong><\/td>\n<td>20.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>The high <strong>IFEval<\/strong> score indicates the model is efficient at following complex, structured instructions, making it suitable for tool use, function calling, and structured data extraction (e.g., JSON). However, the documentation explicitly states that <strong>LFM2.5-350M is not recommended for mathematics, complex coding, or creative writing.<\/strong> For those tasks, the reasoning capabilities of larger parameter counts remain necessary.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img decoding=\"async\" width=\"1512\" height=\"1320\" data-attachment-id=\"78730\" data-permalink=\"https:\/\/www.marktechpost.com\/2026\/03\/31\/liquid-ai-released-lfm2-5-350m-a-compact-350m-parameter-model-trained-on-28t-tokens-with-scaled-reinforcement-learning\/screenshot-2026-03-31-at-10-01-19-pm-2\/\" data-orig-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/Screenshot-2026-03-31-at-10.01.19-PM-1.png\" data-orig-size=\"1512,1320\" data-comments-opened=\"1\" data-image-meta='{\"aperture\":\"0\",\"credit\":\"\",\"camera\":\"\",\"caption\":\"\",\"created_timestamp\":\"0\",\"copyright\":\"\",\"focal_length\":\"0\",\"iso\":\"0\",\"shutter_speed\":\"0\",\"title\":\"\",\"orientation\":\"0\"}' data-image-title=\"Screenshot 2026-03-31 at 10.01.19\u202fPM\" data-image-description=\"\" data-image-caption=\"\" data-medium-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/Screenshot-2026-03-31-at-10.01.19-PM-1-300x262.png\" data-large-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/Screenshot-2026-03-31-at-10.01.19-PM-1-1024x894.png\" src=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2026\/03\/Screenshot-2026-03-31-at-10.01.19-PM-1.png\" alt=\"\" class=\"wp-image-78730\" \/><figcaption class=\"wp-element-caption\">https:\/\/www.liquid.ai\/blog\/lfm2-5-350m-no-size-left-behind<\/figcaption><\/figure>\n<\/div>\n<h3 class=\"wp-block-heading\"><strong>Hardware Optimization and Inference Efficiency<\/strong><\/h3>\n<p>A major hurdle for AI devs is the \u2018memory wall\u2019\u2014the bottleneck created by moving data between the processor and memory. Because the LFM2.5-350M utilizes LIVs and GQA, it drastically reduces KV cache size, boosting throughput. On a single NVIDIA H100 GPU, the model can reach a throughput of <strong>40.4K output tokens per second<\/strong> at high concurrency.<\/p>\n<p><strong>Liquid AI team reports device-specific low-memory inference results that make local deployment viable:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Snapdragon 8 Elite NPU:<\/strong> 169MB peak memory using RunAnywhere Q4.<\/li>\n<li><strong>Snapdragon GPU:<\/strong> 81MB peak memory using RunAnywhere Q4.<\/li>\n<li><strong>Raspberry Pi 5:<\/strong> 300MB using Cactus Engine int8.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Key Takeaways<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Extreme Intelligence Density:<\/strong> By training a 350M parameter model on <strong>28 trillion tokens<\/strong>, Liquid AI team achieved an super high 80,000:1 token-to-parameter ratio, allowing it to outperform models more than twice its size on several benchmarks.<\/li>\n<li><strong>Hybrid LIV Architecture:<\/strong> The model departs from pure Transformers by using <strong>Linear Input-Varying Systems (LIVs)<\/strong> combined with a small number of <strong>Grouped Query Attention (GQA)<\/strong> blocks, significantly reducing the memory overhead of the KV cache.<\/li>\n<li><strong>Edge-First Efficiency:<\/strong> It is designed for local deployment with a <strong>32k context window<\/strong> and a remarkably low memory footprint\u2014reaching as low as <strong>81MB<\/strong> on mobile GPUs and <strong>169MB<\/strong> on NPUs via specialized inference engines.<\/li>\n<li><strong>Specialized Agentic Capability:<\/strong> The model is highly optimized for <strong>instruction following (IFEval: 76.96)<\/strong> and tool use, though it is explicitly not recommended for complex coding, mathematics, or creative writing.<\/li>\n<li><strong>Massive Throughput:<\/strong> The architectural efficiency enables high-speed utility, processing up to <strong>40.4K output tokens per second<\/strong> on a single H100, making it ideal for high-volume data extraction and real-time classification.<\/li>\n<\/ul>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n<p>Check out\u00a0the\u00a0<strong><a href=\"https:\/\/www.liquid.ai\/blog\/lfm2-5-350m-no-size-left-behind\" target=\"_blank\" rel=\"noreferrer noopener\">Technical details<\/a> <\/strong>and<strong> <a href=\"https:\/\/huggingface.co\/LiquidAI\/LFM2.5-350M\" target=\"_blank\" rel=\"noreferrer noopener\">Model Weight<\/a>.\u00a0<\/strong>Also,\u00a0feel free to follow us on\u00a0<strong><a href=\"https:\/\/x.com\/intent\/follow?screen_name=marktechpost\" target=\"_blank\" rel=\"noreferrer noopener\"><mark>Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0<strong><a href=\"https:\/\/www.reddit.com\/r\/machinelearningnews\/\" target=\"_blank\" rel=\"noreferrer noopener\">120k+ ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0<strong><a href=\"https:\/\/www.aidevsignals.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">our Newsletter<\/a><\/strong>. Wait! are you on telegram?\u00a0<strong><a href=\"https:\/\/t.me\/machinelearningresearchnews\" target=\"_blank\" rel=\"noreferrer noopener\">now you can join us on telegram as well.<\/a><\/strong><\/p>\n<p>The post <a href=\"https:\/\/www.marktechpost.com\/2026\/03\/31\/liquid-ai-released-lfm2-5-350m-a-compact-350m-parameter-model-trained-on-28t-tokens-with-scaled-reinforcement-learning\/\">Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning<\/a> appeared first on <a href=\"https:\/\/www.marktechpost.com\/\">MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>In the current landscape of generative AI, the \u2018scaling laws\u2019 have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention with the release of LFM2.5-350M. This model is actually a technical case study in intelligence density with additional pre-training (from 10T to 28T tokens) and large-scale reinforcement learning The significance of LFM2.5-350M lies in its architecture and training efficiency. While the most AI companies has been focused on frontier models, Liquid AI is targeting the \u2018edge\u2019\u2014devices with limited memory and compute\u2014by proving that a 350-million parameter model can outperform models more than twice its size on several evaluated benchmarks. https:\/\/www.liquid.ai\/blog\/lfm2-5-350m-no-size-left-behind Architecture: The Hybrid LIV Backbone The core technical differentiator of the LFM2.5-350M is its departure from the pure Transformer architecture. It utilizes a hybrid structure built on Linear Input-Varying Systems (LIVs). Traditional Transformers rely entirely on self-attention mechanisms, which suffer from quadratic scaling issues: as the context window grows, the memory and computational requirements for the Key-Value (KV) cache increase. Liquid AI addresses this by using a hybrid backbone consisting of: 10 Double-Gated LIV Convolution Blocks: These handle the majority of the sequence processing. LIVs function similarly to advanced Recurrent Neural Networks (RNNs) but are designed to be more parallelizable and stable during training. They maintain a constant-state memory, reducing the I\/O overhead. 6 Grouped Query Attention (GQA) Blocks: By integrating a small number of attention blocks, the model retains high-precision retrieval and long-range context handling without the full memory overhead of a standard Transformer. This hybrid approach allows the LFM2.5-350M to support a 32k context window (32,768 tokens) while maintaining an extremely lean memory footprint. Performance and Intelligence Density The LFM2.5-350M was pre-trained on 28 trillion tokens with an extremely high training-to-parameter ratio. This ensures that the model\u2019s limited parameter count is utilized to its maximum potential, resulting in high \u2018intelligence density.\u2019 Benchmarks and Use Cases The LFM2.5-350M is a specialist model designed for high-speed, agentic tasks rather than general-purpose reasoning. Benchmark Score IFEval (Instruction Following) 76.96 GPQA Diamond 30.64 MMLU-Pro 20.01 The high IFEval score indicates the model is efficient at following complex, structured instructions, making it suitable for tool use, function calling, and structured data extraction (e.g., JSON). However, the documentation explicitly states that LFM2.5-350M is not recommended for mathematics, complex coding, or creative writing. For those tasks, the reasoning capabilities of larger parameter counts remain necessary. https:\/\/www.liquid.ai\/blog\/lfm2-5-350m-no-size-left-behind Hardware Optimization and Inference Efficiency A major hurdle for AI devs is the \u2018memory wall\u2019\u2014the bottleneck created by moving data between the processor and memory. Because the LFM2.5-350M utilizes LIVs and GQA, it drastically reduces KV cache size, boosting throughput. On a single NVIDIA H100 GPU, the model can reach a throughput of 40.4K output tokens per second at high concurrency. Liquid AI team reports device-specific low-memory inference results that make local deployment viable: Snapdragon 8 Elite NPU: 169MB peak memory using RunAnywhere Q4. Snapdragon GPU: 81MB peak memory using RunAnywhere Q4. Raspberry Pi 5: 300MB using Cactus Engine int8. Key Takeaways Extreme Intelligence Density: By training a 350M parameter model on 28 trillion tokens, Liquid AI team achieved an super high 80,000:1 token-to-parameter ratio, allowing it to outperform models more than twice its size on several benchmarks. Hybrid LIV Architecture: The model departs from pure Transformers by using Linear Input-Varying Systems (LIVs) combined with a small number of Grouped Query Attention (GQA) blocks, significantly reducing the memory overhead of the KV cache. Edge-First Efficiency: It is designed for local deployment with a 32k context window and a remarkably low memory footprint\u2014reaching as low as 81MB on mobile GPUs and 169MB on NPUs via specialized inference engines. Specialized Agentic Capability: The model is highly optimized for instruction following (IFEval: 76.96) and tool use, though it is explicitly not recommended for complex coding, mathematics, or creative writing. Massive Throughput: The architectural efficiency enables high-speed utility, processing up to 40.4K output tokens per second on a single H100, making it ideal for high-volume data extraction and real-time classification. Check out\u00a0the\u00a0Technical details and Model Weight.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter\u00a0and don\u2019t forget to join our\u00a0120k+ ML SubReddit\u00a0and Subscribe to\u00a0our Newsletter. Wait! are you on telegram?\u00a0now you can join us on telegram as well. The post Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":80518,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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