Wednesday, AI 101/Concept: let’s discuss Meta Learning

Friday, we continue our Agentic Workflow series with one fascinating development

Hugging Face Grows Arms and Legs
Hugging Face has introduced two open-source humanoid robots – HopeJR (starts at $3000), a full-size bipedal bot with 66 degrees of freedom, and Reachy Mini (starts at $300), a desktop companion with conversational skills. Built after acquiring Pollen Robotics, these bots aim to democratize robotics by staying affordable and transparent. Very exciting to see this development.

Anthropic open-sourcing circuit tracing tools

• In collaboration with Decode Research, they released an open-source library to generate attribution graphs that reveal internal reasoning paths of LLMs. These graphs support models like Gemma-2-2b and Llama-3.2-1b, and are viewable via Neuronpedia’s interactive frontend. The tools allow users to modify feature values, test hypotheses, and trace circuits. The release aims to accelerate interpretability research and understanding of model behaviors across multilingual and reasoning tasks →read more in their blog or try out

• Also – in just five months, Anthropic has tripled its annualized revenue – from $1B to $3B – thanks to surging business demand for AI, especially in code generation. Wow.

Telegram & xAI: Chat Happens

Deal made in heaven: two controversial characters – Pavel Durov and Elon Musk – signed a deal to embed Grok – xAI’s signature chatbot – directly into the Telegram’s app. Telegram also gets 50% of all Grok subscription revenue sold through the platform. The partnership caused TON to surge 18.5%, though it mysteriously jumped hours before the announcement. Market clairvoyance, anyone? Meanwhile, Telegram is also raising $1.5B in bonds, with BlackRock and Citadel backing the play.

Meta and Anduril Suit Up
Meta and defense-tech firm Anduril have joined forces to bring mixed reality to the battlefield. It’s less a gaming headset now, more like a combat-ready technomancer gear. Their XR-powered integration will enhance soldiers’ perception and link directly into Anduril’s Lattice AI system. Privately funded and built from commercial tech, the partnership claims it could save the Pentagon billions – and make Palmer Luckey’s warfighter dreams a vivid, augmented reality. Can we please also have wars solely in Metaverse?

Mistral Gives Agents Their Marching Orders

Mistral AI has launched its Agents API – a toolkit turning AI from passive scribe into active problem-solver. With built-in connectors for code execution, web search, and more, plus orchestration features and persistent memory, it’s built for serious enterprise-grade workflows. From coding and finance to nutrition planning, Mistral is quietly building an ecosystem of autonomous assistants that don't just talk – they do.

Adaptive reasoning model ARM (Fudan University and The Ohio State University) is a model that adaptively chooses among four reasoning formats (Direct Answer, Short CoT, Code, Long CoT) based on task difficulty. Trained with Ada-GRPO, a GRPO variant preventing format collapse, ARM maintains accuracy while reducing token usage by ~30% on average and up to 70%. It achieves ~2× training speedup and supports Adaptive, Instruction-Guided, and Consensus-Guided reasoning modes.

TabSTAR (Technion-IIT) is a tabular foundation model using unfrozen text encoders and target-aware tokens to produce semantically aligned, task-specific embeddings. TabSTAR supports classification and regression without dataset-specific parameters and excels in textual tabular datasets. Evaluated on 50 datasets, it outperforms GBDTs and other TFMs in classification, achieving up to 0.874 normalized AUROC. Pretraining across 350 datasets shows scaling laws, with performance increasing with more data. TabSTAR trains on a single A40 GPU in under 48 hours.

rStar-Coder (Microsoft Research Asia) is a dataset of 418K competitive-level programming problems and 580K long-reasoning solutions, all verified via diverse test cases. They curate 37.7K expert problems, synthesize 380K new ones, and use a three-step test input generation method plus mutual verification for output correctness, achieving 96.8% labeling accuracy. Models trained on rStar-Coder surpass QWQ-32B, with a 7B model scoring 57.3% on LiveCodeBench and 16.15% on USACO 2025, outperforming much larger models.

🌟 REWARDBENCH 2 (Allen Institute for AI (Ai2), University of Washington, and Cohere) is a benchmark of 1,865 unseen human prompts across six domains (Factuality, Precise Instruction Following, Math, Safety, Focus, and Ties) designed to assess reward models (RMs) via best-of-4 accuracy. REWARDBENCH 2 reduces top RM scores by ~20 points vs. its predecessor, improving difficulty and correlation with downstream performance. Evaluations of 113 RMs show strong correlation (r=0.87) with best-of-N sampling, but PPO performance depends on alignment between RM and policy model training. Congrats to our subscribers from Ai2 and Cohere! →read the paper

WebDancer builds end-to-end agentic information-seeking agents using a structured training pipeline of browsing, sampling, fine-tuning, and RL to solve research tasks efficiently →read the paper

Let's Predict Sentence by Sentence lifts LLMs to sentence-level reasoning by predicting semantic and contextual sentence embeddings, reducing inference cost while preserving performance →read the paper

REARANK improves reranking in IR by explicitly reasoning with listwise feedback using reinforcement learning, matching GPT-4 on complex tasks →read the paper

Universal Reasoner adds a plug-and-play reasoning module to frozen LLMs, enabling composable reasoning without retraining or compromising core capabilities →read the paper

PATS adapts LLM reasoning strategy dynamically at the step level based on task difficulty, optimizing both speed and accuracy →read the paper

R2R routes only divergent reasoning tokens to large models while letting small models handle the rest, achieving speedups with minimal accuracy trade-offs →read the paper

Table-R1 applies inference-time scaling to table reasoning using distillation and RLVR, matching GPT-4-level performance with a 7B model →read the paper

MetaMind simulates human Theory of Mind via a multi-agent LLM system, achieving human-level performance in social reasoning tasks →read the paper

Multi-Domain Explainability of Preferences explains LLM preferences across domains using concept-based vectors and a white-box regression model, improving interpretability and alignment →read the paper

Deciphering Trajectory-Aided LLM Reasoning interprets reasoning as meta-learning, showing how LLMs adapt to new tasks via inner-loop optimization mechanisms →read the paper

Advancing Multimodal Reasoning via RL with Cold Start combines supervised fine-tuning and RL to boost multimodal reasoning, outperforming both standalone methods on math/visual benchmarks →read the paper

Surrogate Signals from Format and Length uses format and response length as proxy rewards to train LLMs for math without needing correct answers, improving efficiency →read the paper

Reinforcing General Reasoning without Verifiers trains LLMs using RL without external verifiers, showing strong performance on real-world reasoning benchmarks using reward-free methods →read the paper

Learning to Reason without External Rewards trains LLMs using self-certainty as an internal feedback loop, removing the need for labeled data or reward models →read the paper

The Climb Carves Wisdom Deeper Than the Summit demonstrates LLM robustness to noisy rewards in RL, showing how key reasoning phrases can substitute for ground-truth verification →read the paper

SynLogic synthesizes logical reasoning tasks at scale for RL training, improving general reasoning across math, logic, and coding →read the paper

The Entropy Mechanism of RL for Reasoning LLMs analyzes entropy collapse in RL for LLMs and proposes techniques to preserve exploration during training, boosting performance →read the paper

Unsupervised Post-Training for MLLMs via GRPO improves multimodal reasoning without supervision by using majority voting as self-reward in a scalable RL loop →read the paper

Enigmata provides a synthetic puzzle benchmark and RL training suite to boost logical reasoning in LLMs using verifiable puzzle tasks →read the paper

QwenLong-CPRS compresses long-context inputs using dynamic optimization and outperforms GPT-4o and Claude3 on long-context benchmarks →read the paper

Fast-dLLM adds KV cache and confidence-aware parallel decoding to Diffusion LLMs, achieving 27.6× speedups with minimal quality loss →read the paper

SageAttention2++ accelerates attention via FP8 matrix operations while preserving accuracy, outperforming FlashAttention with 3.9× speedups →read the paper

Shifting AI Efficiency from Model-Centric to Data-Centric Compression advocates token compression over parameter reduction as the future of AI efficiency, laying a new research agenda →read the paper

GraLoRA introduces a granular low-rank adaptation structure that prevents overfitting in PEFT and improves performance over standard LoRA →read the paper

How Does Alignment Enhance LLMs' Multilingual Capabilities? analyzes neuron-level changes in multilingual LLMs after alignment, offering insights into spontaneous cross-lingual adaptation →read the paper

Paper2Poster automates academic poster creation using a multi-agent system that distills papers into structured layouts with minimal cost →read the paper

ZeroGUI trains GUI agents online with zero human input by using VLMs to generate tasks and assess outcomes, enabling self-sufficient GUI automation →read the paper

Discrete Markov Bridge learns discrete data representations with a novel matrix/score learning combo, outperforming baselines on text and image benchmarks →read the paper

Are Reasoning Models More Prone to Hallucination? explores how different training methods affect hallucination in LLMs and links model uncertainty to factuality errors →read the paper