Muse Code and Muse Spark 1.2

Muse released Muse Code (beta), a terminal coding agent powered by its new Muse Spark 1.2 model, which features async background agents, a replay-exact runtime, and bundled skills for planning and execution. Muse Spark 1.2, a coding-focused update to Muse Spark 1.1, was co-trained with Muse Code and includes improvements in code generation, debugging, and long-horizon tasks, with a case study showing iterative GPU kernel optimization over 1,000+ tool calls.

Introducing Muse Code and Muse Spark 1.2 We're excited to release Muse Code beta , a terminal coding agent powered by Muse Spark 1.2, our newest model. This marks our next step toward the frontier, with larger and much more capable models on the way. Install Muse Code on macOS or Linux: Muse Code takes on complex software engineering tasks across large repositories: planning changes, writing code, and validating the results. It can coordinate multiple persistent subagents for each task, solving difficult problems faster, more accurately, and with less intervention. Muse Code Async Background Agents Muse Code operates with a simple agent loop plus a set of async background agents to enhance the main agent's capability. These specialized background agents remain active throughout each session, rather than being spawned for individual tasks, helping avoid redundant information gathering. They carry out next steps and choose when to communicate back to the main agent. Their persistence reduces latency and the need for steering on difficult, multi-step tasks. Runtime Design Muse Code uses a local event log in which every model call, tool run, approval, and edit is appended. This single source of truth makes the runtime replay-exact and restart-safe: after a crash, the agent can resume precisely where it stopped. That ability lets Muse Code take on long-running tasks without being derailed by failures. Bundled Skills Muse Code ships with several default skills. /plan turns a task into an approval-gated plan, /grill stress-tests that plan until it holds up, and /goal works toward successful completion of the specified objective. Muse Spark 1.2 Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1, with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. In Muse Spark 1.2, we significantly scaled up training compute on coding tasks while expanding training environment diversity. The model also maintains its strength in other key areas like general agents. For more details about our evaluations, see our report /static/muse-spark-1-2-methodology . Co-Training With Muse Code We co-trained Muse Spark 1.2 with Muse Code to ensure the model exhibits its best performance and coding usability when paired together. The training included rejection sampled harness trajectories and recipe optimizations for goals, compaction, and subagents, alongside the integration of the Muse Code toolset to maximize harness compatibility. Long-Horizon Muse Spark 1.2 was extensively trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, and auto-research. It leverages planning to sequence work, goal conditioning to maintain direction, and context compaction to retain the knowledge needed to sustain progress. Self-Improvement We also used Muse Spark 1.1 to generate challenging coding environments and instruction-following templates. The model then graded candidate solutions on how well they satisfied those requirements, producing a scalable training dataset for Muse Spark 1.2. This self-improvement loop helped Muse Spark 1.2 follow complex instructions more precisely than its predecessor. Case Study: Kernel Optimization We tested the model's ability to iteratively optimize GPU kernels over 1,000+ tool calls up to 24 hours . Leveraging Muse Code's agentic coding environment, the model writes, compiles, profiles, and progressively improves kernel performance relative to a provided baseline implementation. We benchmarked on KDA and MLA kernels for NVIDIA Hopper GPUs. The agent continues to achieve substantial improvements over the provided baseline implementation. The baseline is the FLA Triton implementation of KDA. Models were prohibited from importing third-party kernel libraries such as FLA directly; instead, they had to apply specialized kernel-optimization knowledge to implement the algorithm in Triton, rather than wrap existing implementations. Muse Spark 1.2 paired a chunk-parallel preparation kernel with a sequential inter-chunk scan, combining standard fusion and tiling with KDA-specific optimizations such as re-centering the gated cumulative decay at the chunk midpoint. Availability Muse Spark 1.2 is available today in Muse Code and in Meta Model API with expanded global access. We have a lot on the horizon, including new harness features and more powerful models. We can’t wait to see what you build Get started with Muse Code https://dev.meta.ai