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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 Planning via Grounded and Reflective Search), a new framework that fundamentally rethinks how planning and search interact in LLM-based agents. Instead of treating MCTS as a brute-force optimizer, SPIRAL embeds a cognitive architecture of three specialized LLM roles directly into the search loop:

• A Planner proposes creative next actions,
• A Simulator grounds those actions by predicting realistic outcomes, and
• A Critic reflects on the results to provide dense, informative reward signals.

This planner–simulator–critic loop transforms search into a guided, self-correcting reasoning process, allowing agents to recover from mistakes, evaluate alternatives more effectively, and plan with far greater robustness.

Paper link: https://arxiv.org/pdf/2512.23167

Repo: https://github.com/IBM/SPIRAL

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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 strategy isn’t obvious from past experience.

The new paper introduces LaMer, a Meta-Reinforcement Learning (Meta-RL) framework designed to give language agents the ability to learn how to explore. Unlike conventional RL agents that learn a fixed policy, LaMer’s Meta-RL approach encourages agents to flexibly adapt by learning from their own trial-and-error experiences. This means agents can better adapt to novel or more difficult environments without needing massive retraining.

We’ll explain:

• Why exploration is critical for long-horizon tasks with delayed or sparse rewards.
• How Meta-RL shifts the focus from fixed policies to adaptable exploration behavior.
• What LaMer’s results suggest about learned exploration and generalization in AI systems.

Whether you’re into reinforcement learning, multi-agent systems, or the future of adaptive AI, this episode breaks down how Meta-RL could help agents think more like explorers—not just pattern followers.

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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 Monte Carlo Tree Search (MCTS) directly into the training loop—not just at inference time. This fundamentally changes how models explore the space of possible solutions: instead of brute-force parameter scaling or longer training runs, DeepSearch uses structured, systematic exploration to dramatically improve learning efficiency.

We break down how DeepSearch:

• Injects tree search into training, enabling richer exploration of reasoning paths.
• Uses a global frontier strategy to prioritize promising reasoning trajectories.
• Improves training-time credit assignment, so models learn not only from success but from strategic exploration itself.
• Achieves impressive results on benchmarks for mathematical reasoning, setting new state-of-the-art performance and using fewer computational resources.

Whether you’re a machine learning researcher, an AI enthusiast, or just curious about the future of intelligent systems, this episode explores how search-augmented learning could redefine how future AI systems master complex reasoning problems.

DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search

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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” modules that specialize in different tasks and, unlike traditional methods like LoRA, can be composed, mixed, and reused because they’re in a principled, orthogonal basis.

During inference, Transformer-Squared runs a two-pass process — the first pass identifies the task or context, and the second pass combines the appropriate expert vectors to dynamically adapt the model’s behavior in real time. Across benchmarks and architectures, SVF consistently outperforms LoRA despite requiring orders of magnitude fewer parameters, and the framework even shows versatility on multimodal tasks like vision-language.

If you’re into efficient adaptation, reinforcement-learning optimization of model components, and self-organizing AI systems, this paper is a big step toward real-time adaptive foundation models. Read the full paper here: https://arxiv.org/pdf/2501.06252

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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 as a hierarchy of nested, multi-level learning processes, each with its own evolving context flow. This perspective sheds new light on how deep models compress information, how in-context learning arises naturally, and how we might build systems with richer, higher-order reasoning abilities.

We explore the paper’s three major contributions:

• Deep Optimizers — A reinterpretation of classic optimizers like Adam and SGD-Momentum as associative memory systems that compress gradients. The authors introduce deeper, more expressive optimizers built directly from NL principles.

• Self-Modifying Titans — A new type of sequence model that learns not just from data, but from its own update rules, enabling it to modify itself during training.

• Continuum Memory System — A unified framework that extends the idea of short- vs long-term memory into a continuous space. Combined with self-modifying models, it leads to HOPE, a learning module showing strong results in language modeling, continual learning, and long-context reasoning.

This episode breaks down what NL means for the future of AI, why it’s mathematically transparent and neuroscientifically inspired, and how it might open a new dimension in deep learning research.

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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:

Self-Questioning – The agent explores environments and generates its own tasks through curiosity-driven interaction, eliminating the need for hand-crafted training data.

Self-Navigating – Instead of random exploration, the agent builds an experience pool, retrieves relevant past solutions, and uses hybrid rollouts that mix experience-guided and vanilla trajectories. They tackle the off-policy learning problem with selective boosting for high-performing trajectories.

Self-Attributing – Fine-grained credit assignment that goes beyond simple trajectory-level rewards, using step-level attribution to figure out which specific actions and states actually contributed to success.

We break down the advantage calculation mechanics, discuss how they handle the inference/learning sample mismatch through experience stripping, and explore why broadcasting trajectory advantages to token-level might be leaving performance on the table.

The results are compelling: their 7B model outperforms much larger baselines on AppWorld and BFCL-v3 benchmarks while reducing training steps by up to 67%. This isn't just another incremental improvement – it's a fundamental shift from human-engineered training pipelines to LLM-guided self-improvement.

Key topics: reinforcement learning for LLMs, experience replay, credit assignment, autonomous task generation, agent systems, GRPO/PPO optimization