Magic

Frontier pretraining is said to be a big-lab-only game. We don’t have 100k chips yet, so there’s only one way: algorithmic efficiency. After compounding for … a while …, our pretraining recipe is now >10x more compute-efficient than that of leading open-weight base models.

We match DeepSeek V4 Pro Base using ~50x fewer FLOPs – that’s around half of GPT3’s pretraining compute, or ~$0.5M on GB200. We continued scaling 10x (~$4M) and meaningfully outperformed all publicly available open base models on perplexity evals. By the scaling laws in Figure 1, training a model this capable would cost >$100M under DeepSeek V4 Pro’s recipe (and this is ignoring how much data exists). Of course, we won’t stop scaling there.

We believe pretraining, agentic RL, and long-context are sufficient to build superhuman coding agents and automate AI R&D. We started with long-context. Today’s blog post is about pretraining.

We measured bits-per-byte loss (a metric that normalizes out differences in tokenizers) on heldout data and fit a scaling law to project how much compute is needed to reach a given level of capability. Better training compute efficiency means stronger models at all budgets.

We evaluated the latest available open-weight base models² from DeepSeek, Moonshot (Kimi), and NVIDIA. Base models for Claude, Gemini, GPT-n, and many others aren’t openly available, but Kimi K3 and Meta’s Muse Spark indicate a 2.5x and 3.3x gain over Kimi K2, respectively. We evaluated logprobs for open models in both vLLM and SGLang on both GB200 and GB300 and found issues with some backends in the process. For further confirmation, we partnered with Fireworks to verify baseline logprobs in their in-house inference engine. Since models can learn their training parser’s characteristics, we built our eval sets using a different parser/OCR than the one our pretraining pipeline uses.

Evaluating generalization

To measure generalization, we evaluated loss on heldout data (Figure 1). Our code evals consist of our own codebase and private codebases we acquired from other startups. For reasoning evals, we generated CoT and step-by-step walkthroughs to heldout, private math problems using Kimi K3 and filtered for correct answers. For text and research, we used recent, low-citation research papers. We removed vendored OSS code and any document with a matching 96-character window of normalized text or Jaccard similarity above a sensitive threshold compared to our training data.³

Evaluating knowledge

In addition to generalization, we are interested in testing our model’s knowledge in key domains to identify gaps in our dataset. For example, we can decompose our heldout research text eval set by subject.

By collecting granular buckets of content (e.g. documentation of a particular software tool or key papers in alignment research) we can get even more precise signals. Unlike for our generalization eval, we don’t want to fully remove much of this information (e.g. key papers in a field) from the pretraining corpus, but we still need to avoid rewarding sequence memorization⁴. To do this, we reworded/summarized these documents using a third-party frontier LLM. To avoid overfitting to granular evals, we created and evaluated them once per model generation; the ones below were made last week.

Magic’s goal is to build the best model for coding and autonomous AI R&D. To intentionally balance data mixing trade-offs, we also evaluate domains we deprioritize (e.g. facts about notable people, local news, or sports/events).

No shortcuts

In late 2024, we trained a small dense model with an architecture designed for very long context windows. Our initial pretraining scale-ups kept blowing up in a wide variety of ways. We learned quickly that we had to build a stable foundation first. Smooth convergence, low-precision training quality equivalent to FP32, fast and stable infra, correct hyperparameter scaling rules. And most importantly: hunt the bugs.

Once we had that in place, we needed to find enough compute efficiency improvements to close the gap to the frontier with less compute. We had a few big bets to start with, but our progress ended up being the multiplicative result of tens of changes across model architecture, optimizer, training objective, and data curation.

NanoGPT speedruns provide a fast feedback cycle to evaluate new ideas, but we found that many things that improve tiny models don’t improve big models. Similarly, we found that some features present in most LLMs can be deleted without harming large scale performance.

To evaluate each model, optimizer, or data change, we train 3 models spanning 2 orders of magnitude of compute. We consider a change worth keeping if its power law fit suggests it will help at scale. Every few weeks, we scaled up to 1/10th of our hero scale and every few months we ran a full-scale hero run (V3, V4, V5 in Figure 4).

To sanity check how pretraining loss translates to post-RL performance, we ran a short math RL run with a 16k CoT budget (Figure 5). All of our RL starts directly from the base model without SFT or distillation.

What’s next

Our pretraining and long-context work is now quite mature. We’ll now scale long-horizon RL, training agents to keep learning after deployment through long-context. We’re also putting significant work towards alignment training techniques that present robust theoretical properties. And last but not least, we look forward to releasing the thing!

Concrete problems we’re tackling include:

• Exploration and credit assignment in long-horizon RL (and systems work to scale up).
• Alignment training against narrowly elicited latent knowledge.⁶
• Further improvements to pretraining.

We are likely the smallest team in the world training trillion parameter models. The impact a single person with strong judgement can have has never been higher. If you want to help build aligned superintelligence, consider joining.