Guide to Open-Source RL Libraries for LLMs

Adaptive RPC Load Balancing via Hyperdimensional Semantic Mapping and Reinforcement Learning This paper introduces a novel approach to Adaptive RPC Load Balancing (ARLB) leveraging Hyperdimensional Semantic Mapping (HDM) and Reinforcement Learning (RL) to dynamically optimize resource allocation within distributed RPC systems. Current load balancing techniques often fail to account for the complex semantic relationships between requests and available services, resulting in suboptimal performance and resource utilization. Our system addresses this limitation by creating a high-dimensional semantic representation of both requests and services, enabling more intelligent and adaptive load distribution. We propose a framework, Hyperdimensional Adaptive Load Balancer (HALB), that dynamically learns optimal routing strategies by observing system behavior and adapting its load balancing policy in real-time. 1. Introduction: The Need for Semantic Context in RPC Load Balancing Remote Procedure Calls (RPC) are a cornerstone of distributed systems, enabling disparate components to communi https://lnkd.in/gkP4t9pu

DeepSeek newly released 𝐃𝐞𝐞𝐩𝐒𝐞𝐞𝐤-𝐎𝐂𝐑 is an open-source vision-language model that's changing the economics of document processing 📄. 📊 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: ➡️ 97% accuracy at 10× text compression (100 vision tokens vs competitors' 256-6,000+) ➡️ Outperforms GOT-OCR2.0 and MinerU2.0 on OmniDocBench ➡️ 2,500 tokens/sec on A100-40G with vLLM 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: ➡️ DeepEncoder (380M): SAM + CLIP + 16× compressor ➡️ DeepSeek-3B-MoE decoder (570M active params) ➡️ 5 resolution modes: 64-1,853 tokens/image Best For: ✅ PDF → Markdown with layout preservation ✅ Multi-language documents (~100 languages) ✅ Charts → structured data (HTML tables) ✅ LLM training data generation ✅ RAG system context compression Quick demo - chart to table - below running entirely on my Mac. #OCR #DeepSeek #AI #GenAI #Document #Processing #ChartToTable #Chart #Recognition

🚀 DSA Series #7 — Graphs: The Real-World Network of Logic 🔗 When I first encountered graphs, I thought — “This is just a complicated tree.” But the deeper I went, the more I realized — graphs are everywhere. They power maps, social networks, recommendation systems, and even compilers. Once you understand DFS and BFS, you don’t just traverse nodes — you start navigating logic networks. 💡 Key Concepts to Master: --> DFS / BFS Traversals --> Cycle Detection --> Topological Sort --> Shortest Path (Dijkstra, Bellman-Ford) --> Minimum Spanning Tree (Kruskal, Prim) Graphs aren’t chaos — they’re strategy, structure, and real-world application combined. They mark the moment you stop solving problems… and start building systems. ⚙️ 👇 Dive into the full breakdown 🔗 https://lnkd.in/gwKsMXkQ #DSA #Graphs #ProblemSolving #CodingJourney #Algorithms #LearnInPublic #TechCommunity

Turns out large context windows aren't all you need -- appreciate this candid assessment of the limitations of large-context windows from Anthropic. The organic distribution of training data means there are comparatively fewer naturally occurring examples of ultra-long-range correlations (e.g. epilogues to 1000 page books) relative to shorter form content. At Brightwave, we find tat thoughtfully selecting not only the passages presented to a model but also the metadata, tools, and other context elements, can have a massive impact on overall system performance. From the article: "Techniques like position encoding interpolation allow models to handle longer sequences by adapting them to the originally trained smaller context, though with some degradation in token position understanding. These factors create a performance gradient rather than a hard cliff: models remain highly capable at longer contexts but may show reduced precision for information retrieval and long-range reasoning compared to their performance on shorter contexts."

🎉 I’m very happy to share that our paper "BERT vs. LLM2Vec: A Comparative Study of Embedding Models for Semantic Information Retrieval" has been accepted at ENIAC 2025! In this work, we conduct a systematic comparison between BERT-based embeddings and the recent LLM2Vec approach for semantic information retrieval. Using six diverse datasets and a 1-Nearest Neighbor (1-NN) classification setup, we evaluate the quality of embeddings with accuracy, precision, recall, and F1-score. Our results show that LLM2Vec models consistently outperform BERT variants, with the unsupervised Mistral-7B-Instruct-v2 achieving the best performance across metrics. Interestingly, we found that unsupervised contrastive learning can surpass supervised approaches, highlighting the strong generalization power of LLM2Vec. Another important finding is that LLM2Vec embeddings are robust to prompt variations, maintaining stable performance across different input formulations, a major advantage for real-world IR applications. Still, BERT offers a good balance between computational cost and performance, making it a practical and efficient choice in resource-constrained scenarios. 🙏 I’m very grateful to my co-author Ricardo Marcacini for his guidance, collaboration, and support throughout this project. 📖 The article will be published soon in the official proceedings.

𝗛𝗼𝘄 𝗪𝗲 𝗕𝗿𝗼𝗸𝗲 𝗗𝗶𝗷𝗸𝘀𝘁𝗿𝗮'𝘀 𝗦𝗽𝗲𝗲𝗱 𝗟𝗶𝗺𝗶𝘁 𝘁𝗼 𝗙𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗵𝗼𝗿𝘁𝗲𝘀𝘁 𝗣𝗮𝘁𝗵𝘀 🚀 For more than 60 years, one algorithm has quietly powered the digital world. Every time you ask a GPS for directions, every time data hops across the internet, every time a network routes a packet to its destination - there’s a good chance Dijkstra’s algorithm is working behind the scenes. 🧭 It’s a beautifully simple idea:  ➡️ Start from your source  ➡️ Explore outward step by step  ➡️ Always choose the next closest point until every destination has the shortest path Elegant. Reliable. And for decades… unbeatable. ⚙️ But even the best solutions come with limits. At its core, Dijkstra’s method relies on sorting nodes by distance — and sorting has a built-in computational cost. That “sorting barrier” became the algorithm’s speed limit. Mathematicians chipped away at it over the years with clever data structures and special-case optimizations. But a fundamental breakthrough for general graphs always felt just out of reach. Until now. ✨ 📜 In a recent paper, researchers Mikkel Thorup, Tianyi Mao, and Ran Duan introduced a new approach that changes how we think about the shortest-path problem. Instead of meticulously sorting every frontier node, their algorithm:  🔹 Clusters nodes into groups  🔹 Uses a limited Bellman-Ford exploration to peek ahead at which nodes truly matter  🔹 Skips strict ordering and focuses only where it counts The result? They avoid the sorting bottleneck - and, for the first time ever, break past Dijkstra’s theoretical speed barrier. 🌐 This isn’t just a math curiosity. Faster shortest-path computations ripple outward into real-world systems: 🔹Smarter routing algorithms that adapt in real time 🔹Network traffic that self-optimizes dynamically 🔹Massive graph analyses that finish in a fraction of the time The gains today are modest - but the door this opens could reshape how we approach routing, infrastructure, and even AI systems that rely on graph traversal. 🤔 I’m curious what you think: Where do you see this having the biggest impact - networking, logistics, AI, or something else entirely? 📖 Read the original article: Quanta Magazine (https://lnkd.in/e7Yytsg3)  📚 Original paper: arXiv:2504.17033 (https://lnkd.in/eH2FZxk2) #Algorithms #Networking #GraphTheory #Innovation #Dijkstra #Routing #AI #ComputerScience #Infrastructure #ABTest 👽

🧐 Remember folks "LLMs do NOT crawl the web, do NOT maintain indexes, and do NOT enforce ranking algorithms at internet scale." Great post from Dan 👇🏻

This: "LLMs have not replaced search. They have simply changed its surface. The invisible machinery of crawling, indexing, retrieval, and ranking remains in the domain of search engines. LLMs are the presentation layer of AI search, a powerful but fallible interface." Read more from Dan Petrovic: https://lnkd.in/gfFykiVR