training

TRL now supports fine-tuning models on agent traces from various sources like Claude Code, Codex, OpenClaw, and Pi, moving towards a standardized stack for training agentic models.

CMU Software Engineering Institute publishes an overview of ML training infrastructure, covering hardware considerations like GPU vs CPU and memory requirements.

Anthropic is hiring 1000 freelance software engineers to train Claude Code, with each task paying $280. The engineers will write prompts, compare code outputs, test model responses, and teach Claude how real developers work.

This article delves into the technical details such as asynchronous and sparse methods used in Cursor training Composer 2 model, and provides a comprehensive analysis of the RL infrastructure.

A hands-on PyTorch curriculum that teaches LLM training from transformer basics through fine-tuning and alignment, including RLHF and GRPO.

A user expresses excitement about working on reinforcement learning at Modal, referencing Modal's announcement of an open-source library and lessons learned for scaling RL training.

In May 2026, a tweet by CJ Zafir teaching ordinary people to fine-tune open source models gained widespread attention, illustrating the trend of training small models as the most underrated AI skill in 2026.

Introduces Eggroll, a low-rank evolution strategy for gradient-free training of spiking neural networks, reducing memory and time overhead while achieving competitive accuracy on N-MNIST.

This article explains the 'Token-In, Token-Out' (TITO) invariant in reinforcement learning for LLMs, highlighting a common error when training multi-turn agents with tool calls. It presents two solutions: using per-model renderers or designing training to avoid re-encoding decoded tokens, emphasizing prefix-preserving chat templates.

Nan Jiang of Modal announces their work on open-source RL frameworks to support frontier open-weights models, highlighting delta compression and remaining challenges in weight sync and cross-cluster training.

A developer reflects on the ease of using Nvidia for local AI tasks versus the satisfaction of getting things to work on Apple Silicon, promoting a 'hungry and foolish' mindset.

This article demonstrates that using stochastic rounding for BF16 optimizer state can match FP32 performance because unbiased errors cancel over time, whereas round-to-nearest stalls due to compounding bias. An experiment with an MLP shows BF16+SR achieves similar loss to FP32 while using less memory.

Built a repository to train a tiny language model (25M parameters) from scratch on 8GB VRAM, with support for MTP but noting limitations of mHC and BitNet.

Sakana AI presents DiffusionBlocks, a method that trains neural networks block-wise by interpreting forward passes as diffusion denoising, significantly reducing memory requirements compared to traditional end-to-end backpropagation.

A critique arguing that training LLMs on human-generated data limits their ability to discover novel solutions via test-time compute, and that true AGI requires models that can explore hypothesis spaces more broadly, similar to AlphaZero.

NVIDIA releases Polar, an open-source infrastructure for black-box agentic reinforcement learning, enabling training of coding agents like Claude Code or Codex with any agent harness or framework.

The article argues that while many are building and selling AI agents, the real value lies in the workflows and training that make them useful, not the underlying technology.

This paper introduces Found in Conversation (FiC), a training framework using View-Asymmetric Self-Distillation to close the multi-turn performance gap in LLMs. The method teaches models to recover single-turn competence from underspecified multi-turn prompts, achieving 92-100% recovery across model families and sizes.

Proposes a 'lift' method for training input-convex neural networks (ICNNs) that uses an unconstrained hypernetwork to emit non-negative inter-layer weights, softening the loss landscape and escaping gradient attenuation, achieving lower test loss than projected gradient descent and softplus reparametrization.

This paper systematically studies scale vectors in LLM normalization layers, showing they optimize training through a self-amplifying preconditioning effect, and proposes three lightweight improvements that enhance performance and scaling behavior with negligible overhead.