Granite 4.2 LLMs: How They're Built

A technical walkthrough of how we built the Granite 4.2 reasoning model family.

TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B . These models are post-trained from Granite-4.1 base models. Granite-4.1 base models were pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, then post-trained with a multi-stage reinforcement learning pipeline . That pipeline includes agentic RL, where the 8B and 30B models learn to act with tools inside real sandboxed environments. Every model has a thinking / non-thinking switch, a low-effort thinking mode that spends a short reasoning budget on easy questions, and native tool calling. All Granite 4.2 models are released under the Apache 2.0 license.

Granite 4.2 is the reasoning-focused release of the Granite language-model family. Earlier Granite releases were strong instruction-following assistants; Granite 4.2 adds explicit reasoning. Every model can produce a chain of thought before its answer and can run in thinking or non-thinking mode depending on how much deliberation a task needs. A low-effort mode falls between the two, spending a short reasoning budget on easy questions.

Granite 4.2 models are post-trained from Granite-4.1 base models. Granite-4.1 base models were trained from scratch on approximately 15 trillion tokens using a five-phase training strategy. Phases 1–2 focus on foundational pre-training, phases 3–4 perform mid-training with progressively higher-quality data annealing, and phase 5 introduces long-context training, extending the context window to 512K tokens . Each phase uses a distinct data mixture and learning-rate schedule, gradually shifting from broad web-scale data toward more curated, high-quality sources.

The pre-training recipe closely follows the previous generation; for a detailed treatment of the data blend, phase schedule, and long-context extension, see the Granite 4.1 blog .

The rest of this post walks through the build: architecture, pre-training, supervised fine-tuning, the multi-stage RL pipeline, and results.

Granite 4.2 models are built on a decoder-only dense transformer architecture with the following core components:

After SFT, we apply a multi-stage, multi-environment reinforcement learning pipeline . Rather than a single RL pass, we run a chain of focused stages spanning many environments: math, code, science, instruction following, tool use, and structured output, then software engineering, terminal use, and web search. Each stage is an independent RL run that targets one capability and warm-starts from the previous stage's checkpoint.

Every stage trains with asynchronous GRPO ( Group Relative Policy Optimization ), so the generator and trainer halves of the loop never block on each other. A pool of generation workers keeps sampling responses and dropping the finished trajectories into a shared buffer; once the buffer holds a full step's worth, the trainer pulls that batch, takes an optimizer step, and streams the updated parameters back to the generator workers without pausing them. A refresh can land partway through a rollout, leaving a single trajectory stitched together from two adjacent policy versions. We allow this instead of paying to prevent it: the workers reuse their existing KV cache rather than rebuilding it after each refresh, and the one guardrail is a limit that keeps them from drifting more than a single update behind the trainer, which bounds how off-policy any sample can get. Whatever mismatch survives that limit is handled in the objective by truncated importance sampling , which clamps the train-versus-generation log-probability ratio to a fixed ceiling so a handful of stale tokens cannot dominate an update.

The three sizes ( 3B, 8B, and 30B ) share the same architectural design and follow the same training pipeline (base model, SFT, then multi-stage RL), each at its own scale. All three are strong reasoners and instruction followers. The clearest capability split shows up in post-training. The 8B and 30B models additionally go through an agentic RL block that teaches them to operate as agents: calling tools, editing and running code, driving a terminal, and searching the web inside real environments. Every model supports native tool calling. Served through an OpenAI-compatible endpoint (for example, with vLLM), it emits tool calls in the OpenAI function-calling format and plugs into agentic harnesses without extra glue. Granite 4.2 is also supported in SGLang, see the SGLang cookbook for a ready-to-serve recipe.

We apply multiple stages of quality control before a sample enters the final SFT mixture. First, data from different sources is normalized and reformatted into a consistent OpenAI Chat format, making the conversation structure and tool interactions uniform across datasets and scaffolds.

Each stage is a separate RL run with a single objective and its own reward signal. When it finishes, its policy is exported to Hugging Face format and becomes the base model for the next stage, so the pipeline is a sequence of warm-starts:

Supervised fine-tuning (SFT) turns the base model into a reliable instruction-following, reasoning, and tool-using assistant. The SFT data mixture combines agentic (31.6%) and non-agentic (68.4%) data, totaling approximately 7.2 million samples, or roughly 100B tokens, of which about 65B are trainable.

The agentic corpus covers a broad range of domains, including software engineering (SWE, 69%), tool calling (12.1%), terminal use (8.0%), math (3.5%), search (0.8%), and action (0.2%). These samples and trajectories are generated using a diverse set of agent scaffolds and harnesses, including OpenHands, OpenCode, Terminus-2, SWE-agent, OpenResearcher, MiniSWE, OpenSeeker, EnvScaler, Gemini CLI, Hermes, Codex, and Goose. The agentic data combines samples from both open-source datasets and our own synthetically generated RL environments, spanning a variety of agent–harness combinations.

The non-agentic corpus consists of several major categories: instruction following (18.8%), coding (18.8%), math (14.6%), multilingual (7.0%), science (5.4%), reasoning (3.0%), and safety (0.8%).