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Samsung's Tiny Recursive Model (TRM) masters complex reasoning With just 7M parameters, TRM outperforms large LLMs on hard puzzles like Sudoku & ARC-AGI. This "Less is More" approach redefines efficiency in AI, using less than 0.01% of competitors' parameters!
Transfusion combines autoregressive with diffusion to train a single transformer, but what if we combine Flow with Flow? 🤔 🌊OneFlow🌊 the first non-autoregressive model to generate text and images concurrently using a single transformer—unifying Edit Flow (text) with Flow…
Reinforcement Learning (RL) has long been the dominant method for fine-tuning, powering many state-of-the-art LLMs. Methods like PPO and GRPO explore in action space. But can we instead explore directly in parameter space? YES we can. We propose a scalable framework for…
🤔Can we train RL on LLMs with extremely stale data? 🚀Our latest study says YES! Stale data can be as informative as on-policy data, unlocking more scalable, efficient asynchronous RL for LLMs. We introduce M2PO, an off-policy RL algorithm that keeps training stable and…
1951年， 中国国防部为整军整党而刊印的 «中国国民党党员守则浅释»： 苏俄帝国主义利用了 共匪朱毛汉奸集团， 要来消灭我们中华民国， 奴役我们中华民族……
Introducing Pretraining with Hierarchical Memories: Separating Knowledge & Reasoning for On-Device LLM Deployment 💡We propose dividing LLM parameters into 1) anchor (always used, capturing commonsense) and 2) memory bank (selected per query, capturing world knowledge). [1/X]🧵
My brain broke when I read this paper. A tiny 7 Million parameter model just beat DeepSeek-R1, Gemini 2.5 pro, and o3-mini at reasoning on both ARG-AGI 1 and ARC-AGI 2. It's called Tiny Recursive Model (TRM) from Samsung. How can a model 10,000x smaller be smarter? Here's how…
Absolutely classic @GoogleResearch paper on In-Context-Learning by LLMs. Shows the mechanisms of how LLMs learn in context from examples in the prompt, can pick up new patterns while answering, yet their stored weights never change. 💡The mechanism they reveal for…
No Prompt Left Behind: A New Era for LLM Reinforcement Learning This paper introduces RL-ZVP, a novel algorithm that unlocks learning signals from previously ignored "zero-variance prompts" in LLM training. It achieves significant accuracy improvements on math reasoning…
This is a super cool topic! I did a class project with a friend for a course on Kernels during senior year in college 🔗: github.com/rish-16/ma4270… Lots of fun connections between kernels and self-attention, especially when learning periodic functions The attention patterns…
How Kernel Regression is related to Attention Mechanism - a summary in 10 slides. 0/1
The paper shows that Group Relative Policy Optimization (GRPO) behaves like Direct Preference Optimization (DPO), so training on simple answer pairs works. Turns a complex GRPO setup into a simple pairwise recipe without losing quality. This cuts tokens, compute, and wall time,…
🚀 Want high-quality, realistic, and truly challenging post-training data for the agentic era? Introducing Toucan-1.5M (huggingface.co/papers/2510.01…) — the largest open tool-agentic dataset yet: ✨ 1.53M real agentic trajectories synthesized by 3 models ✨ Diverse, challenging tasks…
This is a solid blog breaking down how mixture-of-experts (MoE) language models can actually be served cheaply if their design is matched to hardware limits. MoE models only activate a few experts per token, which saves compute but causes heavy memory and communication use.…
Analysis of KL estimator k1, k2, k3 and their use as a reward or as a loss. Partially this is a continuation of previous discussions on it x.com/QuanquanGu/sta…. By the way RLHF is a bit misleading; they did RLVR.
The original GRPO is an off-policy RL algorithm, but its KL regularization isn't done right. Specifically, the k3 estimator for the unnormalized reverse KL is missing the importance weight. The correct formulation should be:
Meta just ran one of the largest synthetic-data studies (over 1000 LLMs, more than 100k GPU hours). Result: mixing synthetic and natural data only helps once you cross the right scale and ratio (~30%). Small models learn nothing; larger ones suddenly gain a sharp threshold…
RL fine-tuning often prematurely collapses policy entropy. We consider a general framework, called set RL, i.e. RL over a set of trajectories from a policy. We use it to incentivize diverse solutions & optimize for inference time performance. Paper: arxiv.org/abs/2509.25424
Exploration is fundamental to RL. Yet policy gradient methods often collapse: during training they fail to explore broadly, and converge into narrow, easily exploitable behaviors. The result is poor generalization, limited gains from test-time scaling, and brittleness on tasks…
🚨🚨New Paper: Training LLMs to Discover Abstractions for Solving Reasoning Problems Introducing RLAD, a two-player RL framework for LLMs to discover 'reasoning abstractions'—natural language hints that encode procedural knowledge for structured exploration in reasoning.🧵⬇️
A look at the history of Reinforcement Learning What is Temporal-Difference (TD) learning? @RichardSSutton introduced TD learning in 1988, and today most widely used RL algorithms, like deep actor-critic, rely on TD error as the learning signal. So, TD learning: ▪️ Allows…
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