Search: “r-machinelearning”

A Reddit discussion thread on r/MachineLearning in which practitioners explore how foundational concepts from Sutton and Barto's *Reinforcement Learning: An Introduction* — including MDPs, policy g...

This Reddit post on r/MachineLearning presents a community-contributed dictionary of contemporary Large Language Model (LLM) terminology, covering terms related to training, fine-tuning, inference, al

A r/MachineLearning discussion thread exploring the potential NLP applications of a large-scale dataset comprising over 20 million Indian legal documents, enriched with citation graphs and pre-compute

Production low-latency autocomplete implementations employ diverse strategies including inference server optimization (tools like vLLM, llama.cpp, NVIDIA Triton), deployment choices (cloud APIs, on-pr

NuExtract3 is a unified 4B vision-language reasoning model for document understanding that combines structured information extraction with image-to-Markdown conversion, suitable for OCR and RAG prepro

Built an open-source AI coding agent that was 7%–75% cheaper than a cold 'claude -p' run on 6/6 well-localized tasks across repositories up to ~82k LOC. The biggest difference: Cold agent: 6.83, 207 t

I have been running some experiments with smaller open-weight LLMs on multiple-choice questions of Swedish medical licensing exams. On a dataset called MedQA-SWE, GPT-4 scored 84% accuracy in 2024 and

NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial. I'm building a SaaS platform in Sri Lanka that handles documents a

I worked on this project (https://github.com/workofart/harness-training) for the past few months to reframe 'Agent-driven Self-improving Harness' to 'Harness Training'. The idea is simple, the harness

Hi everyone, I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before

Paper:https://arxiv.org/abs/2607.19058 Code (GitHub):https://github.com/nuemaan/skewadam Hi everyone, I just published a preprint on a new optimizer designed to tackle the massive VRAM bottleneck in M

Built an open-source AI coding agent that was 7%–75% cheaper than a cold 'claude -p' run on 6/6 well-localized tasks across repositories up to ~82k LOC. The biggest difference: Cold agent: 6.83, 207 t

There are a few reasons why problems from International Mathematical Olympiad function as a good benchmark for LLMs: - The problems are new, not included in the training data of any model - Hard math

I'm a software engineer who mainly builds softwaes/applications, and I'm starting to work on machine learning projects. Since ML workloads often require GPUs, I know services like Google Colab and Kag

While building this workflow a thing that surprised me was that, initially I thought the pipeline was complete: From Goal to → Decompose → Research → Specification → Implementation It successfully bro

We developed a leaderboard ranking frontier model security. There's no shortage of model capability rankings, but we didn't find anything comparable for model security. Yet security is becoming increa

The 2nd Multilingual Conversational Speech Language Model (MLC-SLM) Challenge is an open research competition inviting teams worldwide to participate , featuring free registration with a $20K prize po

This post likely describes a machine learning project implementing QLoRA (Quantized Low-Rank Adaptation) fine-tuning on the Qwen2.5-1.5B model to classify English language proficiency levels according

TL;DR: I’m reproducing the trait-persistence result from arXiv:2606.24014 on one RTX 3090. Before I can test persistence I need to install a trait via RL — and my GRPO run moves the trait only +2.4 po

I have been running some experiments with smaller open-weight LLMs on multiple-choice questions of Swedish medical licensing exams. On a dataset called MedQA-SWE, GPT-4 scored 84% accuracy in 2024 and

NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial. I'm building a SaaS platform in Sri Lanka that handles documents a

I'm a software engineer who mainly builds softwaes/applications, and I'm starting to work on machine learning projects. Since ML workloads often require GPUs, I know services like Google Colab and Kag

Hello guys. I found some problems in current frontier models. And want to share. # math_code_hallucination > Record of a failure caused by combining mathematics and code in a single prompt. --- ## Cas

We developed a leaderboard ranking frontier model security. There's no shortage of model capability rankings, but we didn't find anything comparable for model security. Yet security is becoming increa

KIV is a project shared on r/MachineLearning presenting a drop-in replacement for HuggingFace's `DynamicCache` that enables up to 1 million token context windows on consumer hardware with only 12GB of

This Reddit post discusses a known instability in PPO when advantage estimates operating across different temporal scales (e.g., short-horizon and long-horizon returns) are dynamically routed or mixed

INT3 compression with fused Metal kernels enables large language models to compute attention operations directly on compressed (INT3/INT4) key-value cache representations using custom GPU kernels that

NuExtract3 is a unified 4B vision-language reasoning model for document understanding that combines structured information extraction with image-to-Markdown conversion, suitable for OCR and RAG prepro

Gemini 3.1 Pro and Gemini 3-Pro lead visual reasoning benchmarks , with GPT-5.2, Kimi-K2.5, and GPT-5.2-Pro following . A 2026 evaluation benchmarked 15 leading multimodal models on visual reasoning a

Hi everyone, I'm working on extracting the hierarchical structure of long PDF documents (legal/regulatory text, lots of numbered sections) and would like to gather some feedback on my approach before

My data set is a list of images, each equipped with a a couple sentences of text. A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching

ML research is progressing at breakneck speed where frontier labs in both academia and industry have access to considerably large computes (GPUs). Where do small labs or independent researchers go in

This r/MachineLearning discussion post examines LLM jailbreaks through the lens of social engineering, arguing that the psychological vulnerabilities found in LLMs are not random artifacts but structu

This Reddit post discusses a known instability in PPO when advantage estimates operating across different temporal scales (e.g., short-horizon and long-horizon returns) are dynamically routed or mixed

A researcher on r/MachineLearning built a political benchmark to evaluate how various LLMs handle sensitive geopolitical and politically contentious questions. Key findings include that Kimi K2 (Moons

easyaligner is a forced alignment library designed to be performant and easy to use , leveraging GPU acceleration to align audio with text transcriptions. The tool supports flexible text normalization

This call for papers announces a workshop focused on the growing need for efficient and effective techniques for editing trained models, especially large generative models. The workshop solicits paper

Largest empirical study of AI penetration in academic publishing ever conducted—51%-by-2025 is the most authoritative quantitative marker yet of how thoroughly LLMs have reshaped scientific writing, a

I am trying to learn concepts like On Policy Distillation (OPD), On Policy Self Distillation (OPSD) and how do they compare to RL algorithms like GRPO. There are a lot of papers on this, but because o