Kimi Linear is a hybrid linear attention architecture that outperforms full attention in short-context, long-context, and reinforcement learning scenarios. It utilizes Kimi Delta Attention to achieve this efficiency.

An open-source model combined with proprietary task data and reinforcement learning has been implemented in various real-world scenarios. Bridgewater Associates utilizes AI to analyze a continuous influx of documents to determine their relevance to investment strategies.

The Little Book of Reinforcement Learning provides a concise introduction to reinforcement learning concepts and algorithms. The associated GitHub repository includes the book, Pytorch-based implementations of various algorithms in the algos/ folder, and detailed explanations in the supplementary/ folder.

Ring-Zero scales zero reinforcement learning (RL) to a trillion parameters, enabling emergent reasoning capabilities. This advancement addresses computational constraints that have limited previous studies in zero RL, which utilizes verifiable rewards without human-annotated data.

LeMario is a Joint-Embedding Predictive Architecture (JEPA) model trained on Super Mario Bros to learn world dynamics from pixels and actions. The model successfully passed all initial tests.

Training a single transformer layer can achieve performance comparable to full-parameter reinforcement learning (RL) training. This finding suggests that RL adaptation may not require uniform updates across all layers of large language models.

Ornith-1.0 is an open-source self-improving model for agentic coding, available in configurations of 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE. It achieves state-of-the-art performance on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo, and OpenClaw by utilizing reinforcement learning for solution generation.

MaxProof is a framework designed for population-level test-time scaling in mathematical proof, specifically within the MiniMax-M3 series. It trains three key capabilities: proof generation, proof verification, and critique-conditioned proof repair.

DeepSeek-V4 is now supported for both inference and reinforcement learning (RL) training from Day 0. SGLang and Miles provide the first open-source stack designed for DeepSeek-V4’s hybrid sparse-attention architecture and manifold-constrained hyper-connections, utilizing FP4 expert weights.

Richard Bellman's 1952 paper established the foundation for optimal control and reinforcement learning. His later work in the 1950s connected continuous-time systems to a previously published physical result from the 1840s, formulating the optimal condition as a partial differential equation (PDE).