The research paper details the release of GLM-5.3, a frontier coding and cyber-capable language model developed by Z.ai. The model utilizes the same base architecture as GLM-5.2, with all performance gains attributed to an extensive post-training process focused on long-horizon tasks and complex engineering environments.

Core Methodology and Training Infrastructure

The development of GLM-5.3 centers on the slime framework, an open-source post-training system designed for Reinforcement Learning (RL) scaling. The framework integrates training, rollout, and data buffering into a singular dataflow, allowing for the modular inclusion of verifiers and agentic environments.

Key technical advancements in the pipeline include:

• Environment Scaling: To move beyond short-horizon coding exercises, the team developed pipelines to synthesize end-to-end long-horizon environments. These use a "solver-judge" paradigm where research agents generate task patterns and verifier agents provide binary rewards for RL, closing the gap between artificial benchmarks and actual professional workflows.
• RL Algorithmic Enhancements: The framework incorporates top-p mask, top-k, and full-vocabulary online policy distillation (OPD). To ensure training–rollout consistency, the developers implemented configurations achieving numerical alignment between paths with log-probability differences controlled at the $1e^{-7}$ level, representing a reduction of $>99.99\%$ in inconsistency compared to previous iterations.
• System Throughput: To support large-scale RL, slime employs hierarchical caching using local storage for model states. By utilizing workload-aware heuristics for prefill/decode ratios and concurrency settings, the system achieves a $>2.3\times$ improvement in end-to-end RL training throughput.

Performance and Emergent Capabilities

GLM-5.3 demonstrates significant gains in two primary domains:

• Coding: The model exhibits a $50\%$ improvement on the in-house Z.ai Code Bench over its predecessor. It achieves open-source state-of-the-art results on benchmarks such as *Terminal Bench 3.0* ($28.3$) and *DeepSWE v1.1* ($66.9$).
• Cybersecurity: A notable "emergent" capability is the model's ability to reason across multi-stage exploitation chains. On *CyberGym*, it achieved a $84.5\%$ success rate. Its performance on *ExploitBench* doubled compared to GLM-5.2, moving from $24.4\%$ to $54.4\%$. Testing against real-world, legacy codebases resulted in the identification of $2,436$ vulnerabilities, including issues dating back to 1981, which are now tracked via the Z.ai Security Disclosure Ledger.

Operational Details

GLM-5.3 enforces the use of "thinking" capabilities, requiring users to configure thinking.type to enabled and specify a reasoning_effort level (low, high, or max). The model is currently available for subscribers of the GLM Coding Plan and is integrated into development tools like ZCode, with public model weights scheduled for release two weeks post-launch.