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AI Research Today unpacks the latest advancements in artificial intelligence, one paper at a time. We go beyond abstracts and headlines, walking through architectures, experiments, training details, ablations, failure modes, and the implications for future work. Each episode will choose between one and three new, impactful research papers and go through them in depth. We will discuss the papers at the level of an industry practitioner or AI researcher. If you want to understand the newest topics in AI research but don't have the time to dig through the papers yourself, this is your solution.
7 Episodes
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Send a text Link to arxiv: https://arxiv.org/pdf/2602.04118 Large language models have recently shown impressive reasoning abilities, often learned through reinforcement learning and low-rank adaptation techniques like LoRA. But these approaches still assume that effective reasoning requires relatively large adaptation layers. This new paper challenges that assumption by asking a provocative question: how small can a reasoning update really be? In this episode, we explore Learning to Rea...
Send us a text Large Language Models often struggle with complex planning tasks that require exploration, backtracking, and self-correction. Once an LLM commits to an early mistake, its linear chain-of-thought reasoning makes recovery difficult. While search methods like Monte Carlo Tree Search (MCTS) offer a way to explore alternatives, they typically rely on sparse rewards and fail to fully exploit the semantic strengths of language models. In this episode, we dive into SPIRAL (Symbolic LLM...
Send us a text Episode Paper: https://arxiv.org/pdf/2512.16848 In this episode, we dive into a cutting-edge AI research breakthrough that tackles one of the biggest challenges in training intelligent agents: how to explore effectively. Standard reinforcement learning (RL) methods help language model agents learn to interact with environments and solve multi-step tasks, but they often struggle when the tasks require active exploration—that is, learning what to try next when the best str...
Send us a text In this episode, we unpack DeepSearch, a new paradigm in reinforcement learning with verifiable rewards (RLVR) that aims to overcome one of the biggest bottlenecks in training reasoning-capable AI systems. Traditional reinforcement learning methods often plateau after extensive training because they rely on sparse exploration and limited rollouts, leaving critical reasoning paths undiscovered and unlearned. DeepSearch turns this model training approach on its head by embedding ...
Send us a text In this episode we’re diving into “Transformer-Squared: Self-Adaptive LLMs” — a new framework for adapting large language models to unseen tasks on the fly by tuning only a small part of their weights. The central idea is Singular Value Fine-Tuning (SVF), a parameter-efficient fine-tuning technique that decomposes each weight matrix with Singular Value Decomposition (SVD) and then only trains a small vector that scales the singular values. These vectors become compact “expert” ...
Send us a text NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning proposes a bold answer. Instead of viewing a model as a single optimization problem, NL treats it...
Send us a text https://arxiv.org/pdf/2511.10395 What if AI agents could teach themselves? In this episode, we dive into AgentEvolver, a groundbreaking framework from Alibaba's Tongyi Lab that flips the script on how we train autonomous AI agents. Traditional agent training is brutal: you need manually crafted datasets, expensive random exploration, and mountains of compute. AgentEvolver introduces a self-evolving system with three elegant mechanisms that let the LLM drive its own learning: Se...
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