Tag: cost
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Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables
Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables Designing a hybrid SQL + vector retrieval system without schema changes, data migration, or performance trade-offs The post Building Cost-Efficient Agentic RAG on Long-Text Documents in SQL Tables appeared first on Towards Data Science. Partha Sarkar Go to original source
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GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems
GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost The post GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems appeared first on Towards Data Science. Partha Sarkar Go to original source
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Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning arXiv:2506.04626v1 Announce Type: new Abstract: Motivated by real-world settings where data collection and policy deployment — whether for a single agent or across multiple agents — are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a…
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Advice on processing ~1M jobs/month with LLaMA for cost savings
Advice on processing ~1M jobs/month with LLaMA for cost savings I’m using GPT-4o-mini to process ~1 million jobs/month. It’s doing things like deduplication, classification, title normalization, and enrichment. This setup is fast and easy, but the cost is starting to hurt. I’m considering distilling this pipeline into an open-source LLM, like LLaMA 3 or Mistral,…
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Forget About Cloud Computing. On-Premises Is All the Rage Again
Forget About Cloud Computing. On-Premises Is All the Rage Again Ten years ago, everybody was fascinated by the cloud. It was the new thing, and companies that adopted it rapidly saw tremendous growth. Salesforce, for example, positioned itself as a pioneer of this technology and saw great wins. The tides are turning though. As much…
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Introduction to Minimum Cost Flow Optimization in Python
Introduction to Minimum Cost Flow Optimization in Python Minimum cost flow optimization minimizes the cost of moving flow through a network of nodes and edges. Nodes include sources (supply) and sinks (demand), with different costs and capacity limits. The aim is to find the least costly way to move volume from sources to sinks while…
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Gap-Dependent Bounds for Federated $Q$-learning
Gap-Dependent Bounds for Federated $Q$-learning arXiv:2502.02859v1 Announce Type: new Abstract: We present the first gap-dependent analysis of regret and communication cost for on-policy federated $Q$-Learning in tabular episodic finite-horizon Markov decision processes (MDPs). Existing FRL methods focus on worst-case scenarios, leading to $sqrt{T}$-type regret bounds and communication cost bounds with a $log T$ term scaling…