PODCAST

Best AI papers explained

Enoch H. Kang

Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

technology

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00:16:28

A Definition of AGI

Best AI papers explained ·

2025/10/22

This paper attempts to provide a comprehensive framework grounded in the Cattell-Horn-Carroll (CHC) theory of cognitive abilities, which breaks down general intelligence into ten core, equally weighted cognitive components, such as General Knowledge (K), Mathematical Ability (M), and various forms of Memory (WM, MS, MR). The paper details specific, measurable tasks and examples—often drawn from human-centric tests like AP exams or psychometric assessments—to evaluate AI performance in each area

00:19:55

Provably Learning from Language Feedback

Best AI papers explained ·

2025/10/21

This paper introduces a new formal framework called Learning from Language Feedback (LLF), which addresses the challenge of training AI agents, particularly large language models (LLMs), using rich natural language critiques and guidance instead of traditional scalar rewards. The authors formalize the LLF problem and introduce the transfer eluder dimension as a complexity measure to quantify how effectively language feedback reduces uncertainty about latent rewards, demonstrating cases where

00:16:30

In-Context Learning for Pure Exploration

Best AI papers explained ·

2025/10/21

This paper introduces In-Context Pure Exploration (ICPE), a Transformer-based architecture designed to efficiently solve active sequential hypothesis testing problems, also known as pure exploration. ICPE meta-trains a model to map observation histories to actions and predicted hypotheses, enabling in-context learning to actively gather data and infer the correct hypothesis on new tasks without requiring parameter updates. The paper frames this as splitting the process into a supervised inference

00:14:42

On the Role of Preference Variance in Preference...

Best AI papers explained ·

2025/10/20

This academic paper investigates the concept of Preference Variance (PVar) as a metric for improving the efficiency of Direct Preference Optimization (DPO), a method for aligning large language models (LLMs) with human feedback. The authors establish a theoretical foundation demonstrating that the magnitude of the DPO training gradient is bounded by the PVar of a given prompt, meaning prompts with low PVar contribute minimally to learning. Experimentally, the paper validates that training LLMs using

00:13:38

Training LLM Agents to Empower Humans

Best AI papers explained ·

2025/10/20

This rsearch paper by Sergey Levine's group introduces a self-supervised method for fine-tuning Large Language Model (LLM) agents to be more effective and aligned assistants, particularly in code generation. The core idea is to train agents to maximize the human's empowerment, defined as the user's ability to effect desired changes in the environment, rather than relying on costly explicit human feedback or inferred rewards. The paper details the mathematical connection between their

00:13:20

Richard Sutton Declares LLMs a Dead End

Best AI papers explained ·

2025/10/20

Once again, instead of introducing new research, we discuss what Richard Sutton, a pioneer of reinforcement learning (RL), controversially argues that large language models (LLMs) represent a "dead end" for achieving general intelligence. Sutton contends that LLMs, which rely on imitation learning from vast datasets of human text, lack the ability to learn continuously "from experience" or possess a meaningful goal or ground truth, which are core to RL and animal intelligence

00:15:21

Demystifying Reinforcement Learning in Agentic Reasoning

Best AI papers explained ·

2025/10/19

The research paper systematically investigates how reinforcement learning (RL) can enhance the agentic reasoning capabilities of Large Language Models (LLMs), particularly in tool-integrated environments. The authors conduct a comprehensive empirical study across three main dimensions: data curation, algorithmic design, and reasoning mode to demystify optimal practices for agentic RL. Key findings include that real end-to-end trajectories are crucial for strong Supervised Fine-Tuning (SFT)

00:13:57

Emergent coordination in multi-agent language models

Best AI papers explained ·

2025/10/19

This paper introduces an **information-theoretic framework** designed to determine when multi-agent Large Language Model (LLM) systems transition from simple aggregates to integrated, synergistic collectives. The research utilizes a **group guessing game without direct communication** to experimentally test how different prompt designs—specifically, a control condition, assigning agent **personas**, and adding a **Theory of Mind (ToM)** instruction—influence emergent coordination. Findings suggest

00:16:02

Learning-to-measure: in-context active feature...

Best AI papers explained ·

2025/10/19

This paper introduces Learning-to-Measure (L2M) to address the challenges of meta-Active Feature Acquisition (meta-AFA), a sequential decision-making problem. Traditional AFA methods often struggle with scalability because they are designed for a single task and fail when trained on retrospective data containing systematic missingness in features. L2M overcomes these limitations by formalizing the meta-AFA problem to allow learning acquisition policies across diverse tasks and leveraging a

00:16:11

Andrej Karpathy's insights: AGI, Intelligence, and...

Best AI papers explained ·

2025/10/19

Today, instead of introducing new research, we go deeper into Andrej Karpathy's insights. In his recent interview, he presents his perspectives on the current state and future of Artificial General Intelligence (AGI) and Large Language Models (LLMs). Karpathy argues that AGI is still about a decade away, asserting that the challenges, while tractable, are difficult and require incremental progress across many domains, including better datasets, hardware, and algorithms. He frequently contrasts

00:12:48

Front-Loading Reasoning: The Synergy between...

Best AI papers explained ·

2025/10/18

This research paper, by authors affiliated with NVIDIA, Carnegie Mellon University, Boston University, and Stanford University, focuses on the optimal strategy for incorporating reasoning data into Large Language Model (LLM) training. The central finding challenges the conventional approach of relying solely on post-training, demonstrating that "front-loading" reasoning data during the pretraining phase is critical, yielding a durable 19% average performance gain on expert-level tasks. The

00:17:02

Representation-Based Exploration for Language Models:...

Best AI papers explained ·

2025/10/18

This paper investigates the effectiveness of deliberate exploration in enhancing the reasoning capabilities of large language models (LLMs) trained with reinforcement learning (RL). The authors propose and evaluate a novel representation-based exploration (RepExp) strategy, which uses a bonus derived from the LLM's hidden states to encourage the discovery of diverse and novel behaviors. The study employs a two-pronged evaluation methodology, first testing RepExp in an inference-time setting for

00:16:08

The attacker moves second: stronger adaptive attacks...

Best AI papers explained ·

2025/10/18

The academic paper discusses the critical flaws in current methods used to evaluate the robustness of large language model (LLM) defenses against jailbreaks and prompt injections. The authors argue that testing defenses with static or computationally weak attacks yields a false sense of security, as demonstrated by the fact that they successfully bypassed twelve different recent defenses with an attack success rate exceeding 90% in most cases. Instead, they propose that robustness must be measured

00:19:44

When can in-context learning generalize out of task...

Best AI papers explained ·

2025/10/16

The research empirically investigates the role of pretraining distribution and a new concept of task diversity in the emergence of ICL, particularly using models trained on linear functions. Findings indicate that increasing task diversity causes transformers to shift from a specialized solution to one that can generalize across the entire task space, a transition also observed in nonlinear regression problems. The authors constructed a phase diagram to characterize how task diversity and the

00:13:41

The Art of Scaling Reinforcement Learning Compute for...

Best AI papers explained ·

2025/10/16

This paper studies scaling reinforcement learning (RL) compute for large language models (LLMs), introducing a principled framework to predict performance. The authors develop ScaleRL, a best-practice recipe derived from ablating various algorithmic choices, and demonstrate its predictable scaling trajectory using a sigmoidal function to fit compute-performance curves. Accompanying figures illustrate validation performance over increasing GPU hours (log scale) for different RL configurations,

00:13:58

A small number of samples can poison LLMs of any size

Best AI papers explained ·

2025/10/16

This white paper by Anthropic, UK AI Security Institute, and The Alan Turing Institute demonstrates that a small, fixed number of malicious documents—as few as 250—can successfully create a "backdoor" vulnerability in LLMs, regardless of the model's size or the total volume of clean training data. This finding challenges the previous assumption that attackers need to control a percentage of the training data, suggesting that these poisoning attacks are more practical and accessible

00:17:11

Dual Goal Representations

Best AI papers explained ·

2025/10/14

This paper discusses dual goal representations for goal-conditioned reinforcement learning (GCRL), a novel method for encoding a state based on its temporal distance relation to all other states within an environment. The authors theoretically establish that this representation is sufficient for recovering an optimal goal-reaching policy and is invariant to extraneous noise within the state observations. Building on this theory, they propose a practical implementation using an inner product

00:16:42

Welcome to the Era of Experience

Best AI papers explained ·

2025/10/14

This "bitter lesson"-style position paper by David Silver and Richard S. Sutton introduces a shift in artificial intelligence from the "Era of Human Data" to the "Era of Experience." The authors argue that AI progress is slowing because the available pool of human-generated data is being exhausted, necessitating a new approach where agents learn predominantly from their own self-generated experience and interaction with the environment. This transition is characterized by agents that inhabit streams

00:15:42

Value Flows: Flow-Based Distributional Reinforcement...

Best AI papers explained ·

2025/10/14

This paper introduces "Value Flows," a novel reinforcement learning algorithm that uses flow-based models to estimate the full future return distribution, instead of flattening it to a single scalar value like traditional methods. This approach is designed to provide richer learning signals and better estimations of aleatoric uncertainty (return variance), which is then used to prioritize learning on uncertain transitions. The abstract and text detail how a new flow-matching objective is

00:16:42

Self-Adapting Language Models

Best AI papers explained ·

2025/10/12

This paper introduces Self-Adapting Large Language Models (SEAL), a novel framework that enables LLMs to autonomously improve by generating their own training data and finetuning instructions, termed "self-edits." This adaptation process is driven by a reinforcement learning (RL) loop that rewards the model for generating self-edits that subsequently improve its performance on downstream tasks, contrasting with static models that learn from data "as-is." The authors demonstrate

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Best AI papers explained

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