Udemy - Recursive Language Models and Advanced LLM Architectures 2026-10

Creation Time	Nov. 8, 2026, 2:49 a.m.
Last Access Time	Nov. 15, 2026, 12:33 a.m.
File Size	2.7 GB
Keywords	and 2026-10 Recursive Language Models - Udemy Architectures LLM Advanced
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8 - Section 8 Mixture of Experts/73 - [LAB] Training the MoE and Watching Expert Load With and Without Balance Loss.mp4 41.9 MB
4 - Section 4 Recursive Language Models (RLM)/35 - [LAB] A Minimal RLM Loop by Hand Root Model, Python REPL, llm_query and FINAL.mp4 40.5 MB
8 - Section 8 Mixture of Experts/75 - [LAB] A Real MoE on CPU Granite 3.1 1B-A400M Router Choices Token by Token.mp4 37.6 MB
10 - Section 10 RAG Patterns That Work in Production/97 - [LAB] RAG vs RLM vs Long Prompt on the Same Question Set.mp4 37.2 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/66 - [LAB] A Hybrid Model SSM Layers Plus One Attention Layer, Trained and Compared.mp4 36.8 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/65 - [LAB] Associative Recall The Task That Separates Attention From Pure SSMs.mp4 36.4 MB
10 - Section 10 RAG Patterns That Work in Production/95 - [LAB] End-to-End Local RAG Answers With Citations and a Faithfulness Check.mp4 36.3 MB
6 - Section 6 State Space Models — S4 and Mamba/56 - [LAB] Sequential vs Chunked Scan on CPU Speed, and Keeping the Numbers Stable.mp4 34.5 MB
2 - Section 2 A Transformer Baseline You Built Yourself/15 - [LAB] Training It on CPU Loss Curves, Steps per Second and Where the Time Goes.mp4 34.5 MB
6 - Section 6 State Space Models — S4 and Mamba/55 - [LAB] A Selective SSM Layer From Scratch Discretise, Scan, Gate.mp4 34.4 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/111 - [LAB] Capstone Part 1 One Task, Three Architectures, One Harness.mp4 33.8 MB
6 - Section 6 State Space Models — S4 and Mamba/58 - [LAB] Pretrained Mamba-130m vs Pythia-160m on CPU Same Data and Tokenizer.mp4 33.6 MB
1 - Section 1 The Architecture Landscape and Your Bench/3 - What Advanced Architecture Means in 2026 Attention, Recurrence, Sparsity.mp4 33.3 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/100 - Fair Comparison Rules Same Data, Tokens, Parameters, Seeds — and Bits per Byte.mp4 32.9 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/81 - [LAB] A Content-Addressed Memory Module Trained on Associative Recall.mp4 32.5 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/79 - External Memory Neural Turing Machine, Memory Networks and the DNC.mp4 32.4 MB
10 - Section 10 RAG Patterns That Work in Production/89 - [LAB] Chunking Strategies on One Corpus Fixed, Recursive, Sentence-Aware.mp4 32.3 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/112 - [LAB] Capstone Part 2 Running the Comparison and Reading the Spread.mp4 32.0 MB
8 - Section 8 Mixture of Experts/74 - [LAB] Collapse on Purpose What an Unbalanced Router Looks Like in the Logs.mp4 32.0 MB
8 - Section 8 Mixture of Experts/69 - Sparsely-Gated MoE to Switch Transformer Top-k Routing, Capacity, Token Drops.mp4 31.9 MB
5 - Section 5 Efficient Attention and Linear Transformers/46 - [LAB] Sliding-Window Attention on the Baseline Memory and Quality vs Window.mp4 31.3 MB
8 - Section 8 Mixture of Experts/72 - [LAB] An MoE Feed-Forward Layer From Scratch Router, Top-2 and Weighted Combine.mp4 31.2 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/85 - [LAB] Sliding Window Plus Sinks Keeping Generation Stable Past the Window.mp4 31.1 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/28 - [LAB] A Weight-Tied Looped Transformer vs the Baseline at Equal Parameters.mp4 31.1 MB
4 - Section 4 Recursive Language Models (RLM)/38 - [LAB] The Official rlms Library on a Local Model, and Why a Small Root Fails.mp4 31.1 MB
10 - Section 10 RAG Patterns That Work in Production/94 - [LAB] Retrieval Evaluation A Labelled Set Scored With Recall@k, MRR and nDCG.mp4 30.9 MB
5 - Section 5 Efficient Attention and Linear Transformers/47 - [LAB] A Linear-Attention Layer From Scratch and Its Recurrent Form.mp4 30.9 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/26 - Looped Transformers 2024-2025 Relaxed Recursion and Mixture-of-Recursions.mp4 30.3 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/80 - Memory for Transformers Transformer-XL, Memorizing Transformers, RMT and Titans.mp4 29.8 MB
4 - Section 4 Recursive Language Models (RLM)/34 - What the RLM Paper Reports Benchmarks, Depth Ablations, Costs and Versions.mp4 29.7 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/104 - [LAB] Synthetic Probes Selective Copying, Induction Heads, Multi-Query Recall.mp4 29.6 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/21 - Recursive vs Recurrent From RAAM (1990) to the Recursive Neural Tensor Network.mp4 29.5 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/84 - PagedAttention, Attention Sinks and KV Quantization The Serving View.mp4 29.1 MB
1 - Section 1 The Architecture Landscape and Your Bench/1 - Course Documents and How to Use Them.mp4 29.0 MB
4 - Section 4 Recursive Language Models (RLM)/39 - [LAB] Aggregating a Long Document Chunked Sub-Calls vs One Long Prompt.mp4 29.0 MB
10 - Section 10 RAG Patterns That Work in Production/91 - [LAB] Vector Indexes With FAISS Flat vs HNSW vs IVF, Recall and Latency.mp4 28.9 MB
8 - Section 8 Mixture of Experts/71 - Fine-Grained and Shared Experts DeepSeekMoE, Mixtral, OLMoE and Qwen MoE.mp4 28.9 MB
5 - Section 5 Efficient Attention and Linear Transformers/44 - Low-Rank and Kernel Tricks Linformer, Performer and Transformers Are RNNs.mp4 28.8 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/64 - Hybrid Stacks in Production Jamba, Zamba, Falcon-H1, Nemotron-H, Qwen3-Next.mp4 28.7 MB
8 - Section 8 Mixture of Experts/70 - Load Balancing Auxiliary Loss, Router z-Loss, Expert Choice, Aux-Loss-Free Bias.mp4 28.6 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/106 - Scaling Laws and What a Toy Experiment Can and Cannot Tell You.mp4 28.4 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/109 - The Trade-Off Axes Training Cost, Latency, Memory, Context Length, Maturity.mp4 28.4 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/82 - [LAB] The KV Cache Measured With and Without It, and Its Size Formula.mp4 28.3 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/83 - [LAB] Cache Management in transformers Dynamic, Static, Quantized, Cropping.mp4 28.1 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/102 - [LAB] Throughput, Time-to-First-Token and Memory on CPU Warm-Up and Spread.mp4 28.0 MB
5 - Section 5 Efficient Attention and Linear Transformers/49 - MQA, GQA and Multi-Head Latent Attention Shrinking the Cache, Not the Compute.mp4 27.8 MB
2 - Section 2 A Transformer Baseline You Built Yourself/13 - [LAB] Self-Attention From Scratch in PyTorch, Checked Against the Built-In SDPA.mp4 27.8 MB
4 - Section 4 Recursive Language Models (RLM)/32 - The RLM Idea The Prompt as a Variable in a REPL, Sub-Calls as a Function.mp4 27.7 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/113 - [LAB] Capstone Part 3 Writing the Architecture Selection Report From Results.mp4 27.6 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/27 - HRM and the Tiny Recursive Model (TRM) Small Networks That Iterate.mp4 27.6 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/114 - Where the Field Is Moving Hybrids, Recursive Inference and Test-Time Memory.mp4 27.5 MB
10 - Section 10 RAG Patterns That Work in Production/92 - [LAB] BM25 Plus Dense Hybrid Search With Reciprocal Rank Fusion.mp4 27.2 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/103 - [LAB] Long-Context Tests Needle Sweeps and Position Effects on a Small Model.mp4 27.2 MB
5 - Section 5 Efficient Attention and Linear Transformers/45 - FlashAttention 1 to 4 Exact Attention, IO-Aware — and Why It Is a GPU Story.mp4 27.2 MB
5 - Section 5 Efficient Attention and Linear Transformers/43 - Sparse and Local Patterns Longformer, BigBird and Sliding-Window Attention.mp4 27.1 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/105 - [LAB] lm-evaluation-harness on CPU One Small Task, Two Architectures.mp4 27.0 MB
2 - Section 2 A Transformer Baseline You Built Yourself/14 - [LAB] A Tiny Decoder-Only Transformer Blocks, Norms, Residuals, Causal Mask.mp4 26.9 MB
10 - Section 10 RAG Patterns That Work in Production/88 - RAG From Lewis et al. (2020) to Today The Moving Parts and Their Failure Points.mp4 26.9 MB
6 - Section 6 State Space Models — S4 and Mamba/54 - Mamba's Selective Scan Input-Dependent Parameters, Hardware-Aware Algorithm.mp4 26.7 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/101 - Perplexity Pitfalls Tokenizers, Context Windows and Strided Evaluation.mp4 26.7 MB
6 - Section 6 State Space Models — S4 and Mamba/53 - S4, H3 and Hyena Long Convolutions and the Long Range Arena.mp4 26.7 MB
6 - Section 6 State Space Models — S4 and Mamba/52 - From Continuous Systems to Discrete Recurrence The SSM Equations Made Concrete.mp4 26.7 MB
10 - Section 10 RAG Patterns That Work in Production/90 - [LAB] Embeddings on CPU Choosing a Small Model, Encoding, Checking Its Licence.mp4 26.6 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/23 - [LAB] A Recursive Network Over Binary Trees in PyTorch, Step by Step.mp4 26.6 MB
6 - Section 6 State Space Models — S4 and Mamba/57 - [LAB] Training a Tiny Mamba-Style Model and Adding Its Row to the Results Table.mp4 26.4 MB
6 - Section 6 State Space Models — S4 and Mamba/59 - Mamba-2 and SSD, Mamba-3 What Changed and What Needs a GPU.mp4 26.3 MB
4 - Section 4 Recursive Language Models (RLM)/33 - RLM vs RAG vs ReAct vs CodeAct Where the Long Input Lives, Who Decides Access.mp4 26.1 MB
2 - Section 2 A Transformer Baseline You Built Yourself/12 - [LAB] Data and Tokenisation A Character-Level Corpus and Its Train Val Split.mp4 25.8 MB
8 - Section 8 Mixture of Experts/76 - MoE in Practice Memory, Serving, Expert Parallelism and When Dense Is Better.mp4 25.8 MB
4 - Section 4 Recursive Language Models (RLM)/40 - Stated Limits Cost Blow-Ups, Sequential Calls, Brittle FINAL, REPL Sandboxing.mp4 25.7 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/25 - Universal Transformers and Adaptive Depth The Halting Idea.mp4 25.6 MB
10 - Section 10 RAG Patterns That Work in Production/93 - [LAB] Re-Ranking With a Cross-Encoder What It Adds, What It Costs.mp4 25.4 MB
1 - Section 1 The Architecture Landscape and Your Bench/6 - Reading a Paper's Claim Correctly Hardware, Scale, Setup and Version.mp4 25.1 MB
10 - Section 10 RAG Patterns That Work in Production/96 - Contextual Retrieval, GraphRAG, Self-RAG and CRAG When the Extra Machinery Pays.mp4 25.0 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/62 - RWKV and RetNet RNN Inference, Parallel Training.mp4 24.5 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/63 - Gated DeltaNet and Kimi Delta Attention The Delta Rule Returns.mp4 24.2 MB
2 - Section 2 A Transformer Baseline You Built Yourself/17 - [LAB] The Bench Harness One Tool for Speed, Memory and Quality Across Models.mp4 23.2 MB
1 - Section 1 The Architecture Landscape and Your Bench/5 - The Quadratic Wall Where Attention's Cost and the KV Cache Actually Bite.mp4 22.5 MB
2 - Section 2 A Transformer Baseline You Built Yourself/18 - [LAB] Sampling From the Baseline and Recording Its Row in the Results Table.mp4 22.3 MB
5 - Section 5 Efficient Attention and Linear Transformers/48 - [LAB] Timing Attention on CPU Length Sweeps, Math vs Flash Backend in PyTorch.mp4 21.1 MB
2 - Section 2 A Transformer Baseline You Built Yourself/16 - [LAB] Loss, Perplexity and Bits per Character — Computed, Not Quoted.mp4 20.4 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/24 - [LAB] Tree-LSTM on Tiny Arithmetic Expressions Training and Checking Answers.mp4 19.8 MB
4 - Section 4 Recursive Language Models (RLM)/37 - [LAB] Running the Hand-Built RLM on a Needle Task and Reading the Full Trace.mp4 19.3 MB
1 - Section 1 The Architecture Landscape and Your Bench/8 - [LAB] Building the Lab Environment CPU PyTorch, Transformers, Pinned Versions.mp4 18.7 MB
1 - Section 1 The Architecture Landscape and Your Bench/7 - [LAB] The Bench 8 CPUs, No GPU — Measuring What This Machine Can Do.mp4 17.5 MB
4 - Section 4 Recursive Language Models (RLM)/31 - Context Rot What NIAH, RULER, Lost in the Middle and OOLONG Show.mp4 16.3 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/115 - Section 12 Wrap What You Can Now Build, Measure and Decide.mp4 16.0 MB
1 - Section 1 The Architecture Landscape and Your Bench/9 - [LAB] Opening archlab The Course Repository and Its Empty Results Table.mp4 15.5 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/20 - Section 3 Intro Reusing the Same Weights, Again and Again.mp4 15.2 MB
2 - Section 2 A Transformer Baseline You Built Yourself/11 - Section 2 Intro Every Comparison Needs a Baseline.mp4 14.6 MB
4 - Section 4 Recursive Language Models (RLM)/36 - [LAB] Serving a Local Model for the Loop Ollama's OpenAI-Compatible Endpoint.mp4 14.6 MB
1 - Section 1 The Architecture Landscape and Your Bench/4 - Three Meanings of Recursive Tree Networks, Looped Depth and RLMs.mp4 14.5 MB
10 - Section 10 RAG Patterns That Work in Production/87 - Section 10 Intro Retrieval as External Memory You Can Update.mp4 14.2 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/61 - Section 7 Intro Why the 2025 Models Mix Layer Types.mp4 14.1 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/29 - Section 3 Wrap What Recursion in Depth Buys You, and What It Costs.mp4 14.0 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/110 - Ecosystem Reality Check What vLLM, llama.cpp and transformers Support Today.mp4 13.9 MB
6 - Section 6 State Space Models — S4 and Mamba/60 - Section 6 Wrap Where SSMs Win, and Where Your CPU Disagrees.mp4 13.8 MB
6 - Section 6 State Space Models — S4 and Mamba/51 - Section 6 Intro A Recurrent Model That Trains Like a Convolution.mp4 13.4 MB
4 - Section 4 Recursive Language Models (RLM)/30 - Section 4 Intro When the Prompt Is Too Long to Read.mp4 13.3 MB
3 - Section 3 Recursive Neural Networks — From Trees to Looped Depth/22 - Tree-LSTM (2015) Gates Over a Parse Tree Instead of a Chain.mp4 13.3 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/86 - Section 9 Wrap Memory Inside, Beside and Outside the Model.mp4 13.2 MB
2 - Section 2 A Transformer Baseline You Built Yourself/19 - Section 2 Wrap A Measured Baseline and the Three Measurement Mistakes.mp4 12.6 MB
8 - Section 8 Mixture of Experts/68 - Section 8 Intro More Parameters, Same Compute per Token.mp4 12.6 MB
8 - Section 8 Mixture of Experts/77 - Section 8 Wrap Sparse Compute, Full Memory.mp4 12.3 MB
4 - Section 4 Recursive Language Models (RLM)/41 - Section 4 Wrap When an RLM Is the Right Tool.mp4 12.0 MB
7 - Section 7 Hybrids and Modern Linear Recurrences/67 - Section 7 Wrap Choosing a Mix, Not a Religion.mp4 11.9 MB
5 - Section 5 Efficient Attention and Linear Transformers/50 - Section 5 Wrap Which Attention Trick for Which Bottleneck.mp4 11.8 MB
1 - Section 1 The Architecture Landscape and Your Bench/10 - Section 1 Wrap The Map, the Bench and the Rules of Fair Measurement.mp4 11.7 MB
1 - Section 1 The Architecture Landscape and Your Bench/2 - Section 1 Intro Why Look Past the Plain Transformer.mp4 7.4 MB
5 - Section 5 Efficient Attention and Linear Transformers/42 - Section 5 Intro Making Attention Cheaper Without Losing the Plot.mp4 7.2 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/99 - Section 11 Intro Numbers You Would Bet Your Architecture On.mp4 7.1 MB
12 - Section 12 Decisions, Trade-Offs and the Capstone/108 - Section 12 Intro From Experiments to an Architecture Decision.mp4 6.9 MB
9 - Section 9 Memory-Augmented Networks and the KV Cache/78 - Section 9 Intro Where Does a Model Keep What It Has Read.mp4 6.9 MB
10 - Section 10 RAG Patterns That Work in Production/98 - Section 10 Wrap A RAG Pipeline You Can Defend With Numbers.mp4 6.8 MB
11 - Section 11 Benchmarking and Evaluating New Architectures/107 - Section 11 Wrap The Results Table, Complete and Honest.mp4 6.7 MB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D01_Reading_an_Architecture_Papers_Claim_Checklist_and_Worked_Examples.docx 26.4 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D02_Fair_Architecture_Comparison_Protocol_Rules_bench_py_Harness_and_Pre_Run_Checklist.docx 25.5 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D03_CPU_Lab_Bench_Setup_and_Reproducibility_Checklist.docx 25.1 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D05_Recursive_Language_Models_in_Practice_Minimal_Loop_Trace_Reading_Safe_REPL.docx 24.3 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D04_Recursion_in_Neural_Networks_Field_Guide_Tree_Networks_Looped_Depth_and_RLMs.docx 24.2 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D08_Mixture_of_Experts_Design_and_Router_Health_Checklist.docx 23.8 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D09_RAG_Pipeline_Evaluation_Template_Labelled_Set_Retrieval_Metrics_Faithfulness.docx 22.9 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D11_Architecture_Selection_Report_Template_with_the_Capstone_Worked_Example.docx 22.6 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D07_State_Space_Linear_Recurrent_and_Hybrid_Models_Reference_Card.docx 22.6 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D06_Attention_Cost_and_KV_Cache_Sizing_Worksheet.docx 21.8 KB
1 - Section 1 The Architecture Landscape and Your Bench/1 - D10_Results_Table_and_Experiment_Log_Template.docx 21.3 KB

Udemy - Recursive Language Models and Advanced LLM Architectures 2026-10

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