SplitMoE is an alternative Mixture of Experts architecture that breaks down wide feed-forward layers into smaller, specialized sub-components to improve token representation flexibility and computational modularity.
The World State Generator (WSG) is a model that keeps language agent plans aligned with the rules of their execution environment by rewriting states after failures. It is trained on 226K trajectories extracted from seven synthetic domains where a program enforces rules and verifies goal reachability.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance diagnostic and clinical healthcare reasoning. The training framework first applies rule-guided RL to MedBullets-derived questions for diagnosis, then utilizes 5.3k synthetic multi-turn scenarios with multi-dimensional rubrics for interactive clinical tasks.
Jen Wei encountered a learning rate death spiral while testing AdamW, Muon, and the latest Dion3 implementation within OLMo-core for an upcoming PyTorch Conference talk.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning. The training framework first targets diagnostic accuracy using MedBullets-derived questions and then addresses multi-turn clinical interactions through 5.3k generated scenarios with multi-dimensional rubrics.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning.
TypeSafe AI has launched Jev, a new class of "System One" models designed for production environments. The company's CEO and co-author of the InstructGPT paper, Diogo Almeida, argues that current frontier models have prioritized alignment and refusals over reliability, leading to issues like hallucinations and sycophancy.
MB-Bidram has released an experimental neural architecture called WideNDepth (WND) designed to separate reasoning capabilities from knowledge storage. The model consists of a "Wide part" that functions as memory and a "Depth part" that acts as the reasoner.
A proposal on Hugging Face discusses two interconnected problems in AI: the reliance on training separate large models and the ecosystem's dependence on general-purpose GPUs.
A user on the Hugging Face forums proposes an experiment in progressive knowledge transfer, where a fixed-size student model (Qwen 4B) learns sequentially from increasingly larger teacher models rather than directly from the largest available one.
OpenAI has introduced a new framework for tracking, investigating, and disclosing misalignment in its models, accompanied by six detailed incident reports from reinforcement learning training. The system establishes specific criteria and deadlines for public disclosure, applying even when the behavior is not fully explained or mitigated.
ByteLex researchers built a coordinate space from 11 tokenizer vocabularies to predict which made-up words their byte-level language model would fail on. The map achieved a Spearman correlation of -0.98 with the model's measured per-word accuracy, outperforming corpus-derived statistics.
OpenAI has released Open-1B, a language model trained under a regime where every operation during training is independently reproducible on heterogeneous commodity hardware with bitwise certainty. This approach addresses the reproducibility issues inherent in open-source models by imposing a definite order on sources of nondeterminism, such as GPU kernel reductions and data batch ordering.
TRL v1.14 introduces LoRA support to the AsyncGRPOTrainer, allowing it to train a Low-Rank Adaptation adapter and synchronize only that small file to vLLM inference workers. This architecture decouples training and generation jobs on separate machines by using a shared Storage Bucket as a filesystem bridge, eliminating the need for NCCL or direct network communication between nodes.
The user pop123-ux has published a learning repository containing 38 runnable notebooks designed to help users understand the Hugging Face stack by progressively removing high-level abstractions. The collection covers a path from core components like transformers and tokenizers into fine-tuning, PEFT, reasoning/RL, and end-to-end project work.
Together AI has expanded its fine-tuning service to support a wider range of open-weight models, including GLM-5.3 and Kimi K2.7, while introducing live experiment tracking and finer controls over the training process.
Researchers from Google and several Japanese universities have introduced ToolGrad, a framework that inverts the traditional pipeline for generating tool-use datasets by constructing verified API chains first and then writing matching user queries. This approach aims to eliminate the inefficiency of query-first methods that often fail during agent exploration.
Researchers introduce ExecCritic, a framework that combines a test-verify-revise scaffold with role-specific reinforcement learning to enhance coding agents. The system separates test construction from source-code repair, using a Test agent to generate repository-native tests and a Repair agent to revise code based on execution feedback.
memaudit is a tool that integrates with the TRL library to verify whether fine-tuning models on private data has led to memorization. It works by injecting calibrated decoy secrets into the dataset and monitoring loss during the training process.
A demonstration shows that freezing key-value (KV) states from a guide text allows a Qwen2.5-3B-Instruct model to apply new instructions without including the guide in the live prompt. The method involves running a guide through the model once, freezing its KV cache, and then generating responses on top of that hidden prefix.