darthy @geekDarthy
Machine learning researcher in deep learning, computational probability, inference, and causality. Sydney, New South Wales Joined December 2014-
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Scaling laws in deep RL? Turns out that batch size, learning rate, and UTD (update-to-data) for getting the most efficient and scalable deep RL has predictable relationships. Checkout the analysis in new work by @_oleh & collaborators: arxiv.org/abs/2502.04327
After more than a year of working on SFT, it's clear — it’s just overfitting to in-domain tasks and lacks true generalization. RL is the real future of intelligent systems. 🌟🤖 SFT is out, the RL revolution is in 🚀🔥
Imagine creating custom datasets and training AI models WITHOUT writing a single line of code. We did and made it a reality. @huggingface Synthetic Data Generator Blog: huggingface.co/blog Space: huggingface.co/spaces/argilla… GitHub: github.com/argilla-io/syn…
Synthetic data and iterative self-improvement is all you need. No humans needed in the evaluation loop. This paper introduces a self-improving evaluator that learns to assess LLM outputs without human feedback, using synthetic data and iterative self-training to match top…
Brilliant paper from @Meta having the potential to significantly boost LLM's reasoning power. Why force AI to explain in English when it can think directly in neural patterns? Imagine if your brain could skip words and share thoughts directly - that's what this paper achieves…
Microsoft Phi-4 is announced! It's a 14B parameter LM trained heavily on synthetic data, with very strong performance, even exceeding GPT-4o on GPQA and MATH benchmarks! Currently available on Azure AI Foundry, will be on HuggingFace next week
Training Large Language Models to Reason in a Continuous Latent Space Introduces a new paradigm for LLM reasoning called Chain of Continuous Thought (COCONUT) Extremely simple change: instead of mapping between hidden states and language tokens using the LLM head and embedding…
Text-to-SQL has been my passion since Yale Spider 1.0! But as LLMs master it, real-world complexity demands more. 🚀After a year of work, Spider 2.0 shows the gap: o1 achieves just 17%! The path to production deployment is still long but exciting! more👉spider2-sql.github.io
Text-to-SQL has been my passion since Yale Spider 1.0! But as LLMs master it, real-world complexity demands more. 🚀After a year of work, Spider 2.0 shows the gap: o1 achieves just 17%! The path to production deployment is still long but exciting! more👉spider2-sql.github.io https://t.co/xq2E2RDZmV
1/2 Critic-RM: A Self-Critiquing AI Framework for Enhanced Reward Modeling and Human Preference Alignment in LLMs Critic-RM, developed by researchers from GenAI, Meta, and Georgia Institute of Technology, enhances reward models through self-generated critiques, eliminating the…
I am happy to announce that the first draft of my RL tutorial is now available. arxiv.org/abs/2412.05265
It was a huge week of AI and LLM papers. Here are the top ML Papers of the Week (Dec 2-8): - Genie 2 - GenCast - OpenAI o1 - Auto-RAG - Reverse Thinking - Retrieval-Augmented Reasoning for LLMs Read on for more:
5). Auto-RAG - an autonomous iterative retrieval model with superior performance across many datasets; Auto-RAG is a fine-tuned LLM that leverages the decision-making capabilities of an LLM. x.com/omarsar0/statu…
5). Auto-RAG - an autonomous iterative retrieval model with superior performance across many datasets; Auto-RAG is a fine-tuned LLM that leverages the decision-making capabilities of an LLM. x.com/omarsar0/statu…
7). Challenges in Human-Agent Communication - present a comprehensive analysis of key challenges in human-agent communication, focusing on how humans and AI agents can effectively establish common ground and mutual understanding. microsoft.com/en-us/research…
OpenAI announced a new RL finetuning API. You can do this on your own models with Open Instruct -- the repo we used to train Tulu 3. Expanding reinforcement learning with verifiable rewards (RLVR) to more domains and with better answer extraction (what OpenAI calls a grader, a…
I will be at #NeurIPS2024:1️⃣Dec 10 (Tue) 9:30am-12: Our Tutorial "Causality for LLMs" w/ Sergio Garrido + Panel w/ @Yoshua_Bengio @bschoelkopf @_jasonwei @Swarooprm7 @giambattista92 2️⃣Dec 11 (Wed) 11am-2pm: Our GovSim Poster (Tragedy of Commons for LLM Agents) 3️⃣Dec 13 (Fri)…
I will be at #NeurIPS2024:1️⃣Dec 10 (Tue) 9:30am-12: Our Tutorial "Causality for LLMs" w/ Sergio Garrido + Panel w/ @Yoshua_Bengio @bschoelkopf @_jasonwei @Swarooprm7 @giambattista92 2️⃣Dec 11 (Wed) 11am-2pm: Our GovSim Poster (Tragedy of Commons for LLM Agents) 3️⃣Dec 13 (Fri)… https://t.co/CewJTJQ432
Learn Rust 🦀 from scratch with the comprehensive guide created by @Android team. 🔗 in comments From the basics to more advanced topics like concurrency & bare-metal programming, you'll find everything you need to start with Rust. PS: The table of contents is well structured!
Natural Language Reinforcement Learning (NLRL) redefines Reinforcement Learning (RL). The main idea: In NLRL, the core parts of RL like goals, strategies, and evaluation methods are reimagined using natural language instead of rigid math. What are the benefits? - NLRL uses not…
Consolidated insights on LLM fine-tuning - a long read across 114 pages. "Ultimate Guide to Fine-Tuning LLMs" Worth a read during the weekend. Few ares it covers 👇 📊 Fine-tuning Pipeline → Outlines a seven-stage process for fine-tuning LLMs, from data preparation to…
🚨LLM Reasoners 🧠 A library for LLMs to do advanced reasoning, including latest algorithms: - Reasoning-via-Planning (RAP) 🎶 - Tree-of-Thought (ToT) 🌴 - beam search, and more All in unified perspective of world models🌎 and reward🥇 More alg & results coming soon!
🚨LLM Reasoners 🧠 A library for LLMs to do advanced reasoning, including latest algorithms: - Reasoning-via-Planning (RAP) 🎶 - Tree-of-Thought (ToT) 🌴 - beam search, and more All in unified perspective of world models🌎 and reward🥇 More alg & results coming soon! https://t.co/OtxL3oUF9a

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