microsoft/FrogNano-4B-2609

FrogNano

Model summary

|Field | Value | --- | --- | | Developer | Microsoft Corporation | | Description | FrogNano is a compact repository-level coding agent derived from Qwen/Qwen3.5-4B. It is post-trained exclusively with reinforcement learning on approximately 1,500 synthetic software-engineering tasks generated, validated, and calibrated against the evolving policy using TaskPilot. FrogNano operates through the lightweight five-tool Leaf harness. Its agent-specific post-training uses no stronger-model solution trajectories, actions, reasoning traces, or patch targets. | | Model architecture | *Dense 32-layer hybrid Gated DeltaNet/gated-attention causal language model; vision components are inherited but not post-trained.* | | Parameters | *500M-5B* | | Inputs | *Text only for* *FrogNano’s* *intended and evaluated use, including natural-language instructions, source code, and textual tool outputs.* *FrogNano's* *main benchmark evaluations use a combined interaction context of approximately 131K tokens, including room for generated output. The upstream checkpoint* *contains* *image and video input components, but* *FrogNano* *did not post-train or evaluate those modalities and does not claim them as supported.* | | Outputs | *Text, including natural-language responses, reasoning text, source code, and structured tool calls. The validated FrogNano configuration allows up to 8,192 generated tokens per assistant turn.* | | Context length | Approximately 131K tokens in the evaluated FrogNano coding-agent configuration. | | Training Dates | *Jun* *2026* *to* *Aug* *2026* | | Release date | *22-SEP-2026* | | Release date in the EU (if different) | | | License | Apache License 2.0. FrogNano is derived from Qwen/Qwen3.5-4B; applicable upstream copyright and attribution notices are retained. | | Model dependencies: | Qwen/Qwen3.5-4B | | List and link to any additional related assets | Technical report; Leaf harness. | | Acceptable use policy | *N/A* |

1. Model overview

FrogNano is derived from Qwen/Qwen3.5-4B, a general-purpose post-trained model designed for language, reasoning, coding, agentic, and multimodal tasks. FrogNano inherits Qwen3.5-4B's dense 32-layer hybrid Gated DeltaNet and gated-attention architecture, but its additional post-training is text-only and focused on repository-level software engineering. The model is further trained using reinforcement learning on approximately 1,500 synthetic SWE task environments generated and calibrated against the evolving policy using TaskPilot. Training uses the five-tool Leaf harness and executable test-based rewards over complete multi-turn coding trajectories.

The additional post-training is intended to improve long-horizon repository navigation, debugging, code editing, test execution, and patch generation in a compact 4B model. Unlike approaches based on behavioral distillation, FrogNano does not train on stronger-model solution trajectories, actions, reasoning traces, or patch targets. This specialization also introduces limitations and risks: performance is sensitive to the Leaf harness and test quality, training data are Python-heavy and primarily English, and generated patches may be incorrect or insecure despite passing available tests. When integrated with the Leaf harness, FrogNano generates structured tool calls that can propose repository changes. Leaf executes authorized tool calls within an isolated repository environment to produce a candidate patch; FrogNano does not itself deploy the changes. Any resulting patches require human review, regression testing, and security validation before use or deployment.

1.1 Key model capabilities

• Repository-level software issue resolution from natural-language task descriptions and existing source code.

• Codebase navigation, cross-file code understanding, debugging, bug fixing, and feature implementation.

• Generation of structured Leaf tool calls that the harness executes in an isolated repository environment to inspect and search files, edit code, run shell commands, and run tests.

• Iterative generation and refinement of multi-file patches using command and test feedback.

• Long-context processing of text and source code, with a supported combined context length of approximately 131K tokens.

1.2 Alignment approach

FrogNano starts from the post-trained Qwen3.5-4B checkpoint and retains its upstream alignment characteristics, subject to changes introduced by subsequent reinforcement learning. FrogNano-specific post-training did not use dedicated safety-preference, refusal, harmful-content, or adversarial datasets. Instead, it applied executable-reward reinforcement learning to approximately 1,500 synthetic repository-level software-engineering tasks generated and calibrated through TaskPilot; generated task tests and existing regression tests rewarded functional correctness, reliable tool use, and preservation of existing behavior. This process was designed primarily for coding performance and regression avoidance, not for broad content-safety or cyber-misuse prevention, so FrogNano should not be considered independently safety-aligned for unrestricted autonomous deployment.

2. Usage

2.1 Primary use cases

FrogNano is intended for software-engineering research and human-supervised development. Given an authorized repository snapshot and an English natural-language issue, the model generates text and structured Leaf tool calls. The Leaf harness executes those calls within an isolated repository environment, enabling iterative file inspection and search, code editing, shell-command and test execution, and production of a candidate patch. This workflow supports bug diagnosis and repair, scoped feature implementation, regression fixing, test-driven code maintenance, and research on long-horizon coding agents. A candidate patch is a proposal, not an approved change, and requires qualified human review and independent regression and security testing before use or deployment.

FrogNano is best suited to English-language, Python-heavy repositories with reproducible environments, clearly scoped tasks, and executable test suites. It is intended for use within the sandboxed Leaf harness or a comparably controlled environment, with generated changes reviewed by a qualified developer and subjected to regression and security testing before...