Training Foundation Models with a Single Algorithm

This paper explores how sparse autoencoders can be applied to user embeddings to uncover hidden behavioral modes without needing raw interaction data or text-aligned training. By treating user representations as sparse superpositions of interpretable concepts, this post-hoc, model-agnostic framework enables zero-shot natural language audience retrieval and fine-grained characterization. Extensive validation on synthetic data, e-commerce records, and transformer-based sequential recommenders demonstrates that sparse autoencoders successfully recover underlying behavioral structures while outperforming classical dimensionality reduction methods in reconstruction fidelity, ranking quality, and feature diversity.

Foundation models are traditionally trained through a sequential pipeline of distinct phases, including pretraining, fine-tuning, and reinforcement learning. Researchers demonstrate that this algorithmic separation is unnecessary by framing these diverse phases as maximum-likelihood fitting to evolving data targets under a unified training system. This approach integrates static corpora and model-generated responses into a single objective, transforming training design into a strategic problem of data composition, timing, and mixing proportions. Extensive experiments reveal that unified training improves task accuracy, compute efficiency, and generalization while providing a superior starting point for continual learning.

Frontier large language models demonstrate a surprising capacity for weight-space programming, meaning they can naturally comprehend, evaluate, and modify the raw parameters of neural networks without relying on traditional gradient-based training. Through this paradigm, advanced systems like GPT-6 Astra can analyze a model's numeric weights to accurately determine its intended function, such as identifying a specific game or recognizing the training language from intermediate transformer blocks. Furthermore, these language models can directly edit or construct functional neural network weightsfrom scratch to solve physical control tasks or outperform heavily optimized reinforcement learning agents. When applied to larger language models, such targeted weight corrections can successfully eliminate repetitive reasoning loops and significantly enhance mathematical reasoning capabilities. Ultimately, this approach establishes a direct bridge between natural language instructions and continuous weight spaces, offering new possibilities for mechanistic interpretability and efficient model optimization.

This paper investigates how large language models unlock latent capabilities through fine-tuning, reframing the process as an information-constrained problem. By analyzing parameter-efficient fine-tuning using randomly chosen parameters, the authors demonstrate that training a tiny fraction of a model's weights—often as few as tens to thousands—can recover the vast majority of its full performance gap. This behavior follows a consistent logistic scaling relationship across various tasks, model sizes, and families, independent of specialized search methods. Furthermore, the study applies Rissanen's Minimum Description Length (MDL) principle to distinguish true capability elicitation from teaching new skills, offering insights that can improve capability forecasting and safety evaluations.

This paper introduce The Trinity, a recursive self-improvement framework designed to scale multimodal intelligence by coupling automated environment generation with agent evolution. Instead of relying on traditional task-specific optimization, the system groups diverse tasks into a hierarchical capability tree based on shared underlying requirements. Through a process called Capability-Driven Real2Sim2Real, the framework constructs simplified, verifiable simulation worlds where specialized Harness configurations continuously refine their reusable modules. A Capability-Aware Harness Router then utilizes public task information and verified execution experience to match new tasks with the most suitable Harness branch. Ultimately, this approach successfully improves performance across numerous training and unseen benchmarks while demonstrating effective transfer to physical, real-world embodied settings.

Training advanced machine learning models typically relies on vast, heterogeneous datasets containing noisy, adversarial, or mismatched data. To address this challenge, the authors introduce dataweighting, a scalable framework that assigns specific weights to training examples based on their actual impact on performance. Instead of relying on manual heuristics or aggressive data filtering, this method uses metagradient descent to dynamically optimize training weights against a small, high-quality target dataset. Furthermore, the approach implements parametric dataweights that successfully transfer across different model scales and generalize to previously unseen training inputs. Ultimately, this technique improves model performance across various applications, successfully integrating synthetic supervision and optimizing complex downstream objectives like ranking accuracy.

Recent research explores performance prediction and model routing in large language models by studying when and why an AI's errors are predictable. The authors demonstrate that any performance predictor surpassing a model's self-confidence can directly improve the model's answer loss through recalibration. Furthermore, a predictor's advantage is strictly bounded by the model's suboptimality combined with the additional parameter capacity of the predictor. Empirical experiments across multiple benchmarks reveal that less optimal models yield greater prediction and routing advantages, which naturally diminish after task-specific finetuning. Finally, these theoretical limits extend to two-model cascades, showing that stronger routing advantages emerge in suboptimal models before they absorb the benefits of further training.

We discuss ASTEROID, a novel student-teacher framework designed to address the challenges of sequential decision-making under partial observability. By combining on-policy context collection with expert supervision, the student policy learns effective in-context exploration without requiring expensive online reinforcement learning or impractical full-coverage offline datasets. Across various simulated and real-world environments, this iterative approach significantly outperforms traditional baselines and frontier models in sample efficiency and test-time performance.

Large language models often exhibit unexpected behavioral fluctuations during pre-training, repeatedly shifting between memorizing shallow patterns and demonstrating true generalization, a phenomenon termed mode-hopping. This instability occurs independently of standard optimization metrics and persists even after training on trillions of tokens across various model scales. Consequently, tracking these hidden generalization dynamics using targeted evaluation suites enables researchers to identify superior intermediate checkpoints and strategically curate pre-training data to improve reasoning and alignment robustness.

Automated research agents powered by large language models often experience performance plateaus during open-ended discovery tasks as their accumulated history makes their edits overly conservative. To overcome this limitation, researchers introduce a novel strategy called forgetting, which periodically discards previous agent sessions and candidate search histories while retaining only the single best solution found so far. When combined with a collaborative multi-agent team setup that allows asynchronous communication, this reset mechanism clears out unproductive contextual baggage and unblocks fresh exploration. Experimental evaluations across diverse domains—including heuristic programming, algorithm design, kernel optimization, and machine learning training—demonstrate that teams utilizing forgetting consistently outperform single-agent baselines and parallel configurations. Ultimately, the findings emphasize that managing what an automated discovery agent is allowed to remember is just as critical to its overall success as the amount of computational resources provided.