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MTS · 2 min · Oct 28, 2026

Mercor published the steps behind its 400-billion-parameter run

Edward Hu · Head of AI Modeling, Mercor

Edward Hu describes a 400-billion-parameter model trained on Apex Agents and what Mercor released besides the training data.

In this episode

Full analysis
  1. 9:22Benchmarks can punish capable models for unfair reasonsPro pick 6 min
  2. 14:52Realistic AI evaluations must reflect how work happensPro pick 3 min
  3. 5:29Mercor uses training runs to validate its data3 min
  4. 17:29Mercor published the steps behind its 400-billion-parameter run2 min
Up next · MoonshotsRobot task costs fell 84% in two months6:51
“We open-sourced the ways, the traces, and most importantly, the actual steps it took for us to get there.”
Edward Huat 18:04 · MTS
18:04

Takeaways

  1. The September release trained a 400-billion-parameter model on Apex Agents.
  2. Mercor released the weights, traces and training steps, but not the data.
  3. Systems checks, data checks and ablations preceded the final RL run.

About this segment

Edward Hu describes a 400-billion-parameter model trained on Apex Agents and what Mercor released besides the training data. Systems and data checks, ablations and trial runs came before the final RL run.

Edward Hu

Edward Hu is Head of AI Modeling at Mercor and former OpenAI researcher.

Leads model training and research at Mercor; invented LoRA and μTransfer at Microsoft.

  • Theo Jaffee, Host · Co-founder & host, MTS

    One of the first members of a16z's new media team; earlier hosted the Emergent Ventures-backed Theo Jaffee Podcast.

  • Sophia Dew, Host · Host, MTS

From “AI Outperforms Junior Accountants on Real Work | Edward Hu”, Oct 28, 2026. Watch the full episode

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 17:32

    Fascinating. That was what I want to ask you for our last question, is what's next for you guys at Mercore? What can people expect to see over the next few months? One of the proudest work among many that I've done on the team is the open model release we did back in September, where we trained a 400-billion-parameter Quinn model on top of a post-trained checkpoint on Apex agents, and we demonstrated a significant improvement, but also we open-sourced everything except for the training data.

  2. 18:08

    We open-sourced the ways, the traces, and most importantly, the actual steps it took for us to get there. And it turns out the big run was only a small part. We have many de-risking steps on the systems level, on the data level and we did many ablation studies and trial runs before we did the final RL run and we believe open source research like that can really help people demystify what it takes to train a model at scale and you can expect another work in that vein in the next month or so and we also have many benchmarks like the one I just talked about and also not just the frontier of AI's economic value in organizations, in work environments, but also for the frontier of science.

  3. 18:55

    And we also have a very exciting research in the economic space. For example, how do we map the entire economy into benchmarks? And it turns out we haven't mapped most of the economy. This is so fascinating. I would argue that McCoy is one of the best places to see what's actually happening at the frontier of AI. how are these models, how is this intelligence going to be diffused into society? What is their actual impact on the tasks that we're using them for? And what could be their impact in society?

  4. 19:29

    So super fascinating stuff. Edward, I really wanted to thank you for joining us today