Show Notes
As I’ve been recapping fundamentals of post-training to wrap up my RLHF / Post-training book I knew I needed to get Finbarr Timbers back on the podcast to talk about the state of play. Over the last few months we’ve had many discussions on what we’d need to do to take an Olmo-style recipe to the frontier, supported by Finbarr’s extensive reading of recent model technical reports. To prepare for this, I put together a summary slide deck on the key post-training recipes historically — the path from InstructGPT to today — and today — the key open frontier models. This deck is summarized below as the technical summary, but we do spend 20-35 minutes on it in the podcast, so watching on YouTube is likely the best experience for this one. I previously interviewed Finbarr in December of 2024, shortly after the release of o1 and Tülu 3 (and before he joined Ai2) on the “We are so back” era of RL. Chapters: * 00:00 Introduction & Olmo reflections * 06:28 Post-train recipes review (history) * 23:00 2026’s model recipes (MiMo Flash, DeepSeek V4, GLM 5, Kimi K2.6, etc.) * 39:05 Open-ended post-training discussions * 48:22 Career advice in the LLM race Listen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here. For more educational post-training videos, see the course I’m putting together. Technical Summary These are notes cleaned up from a slide-deck created with AI assistance — mostly useful as a discussion topic and reference. The shape of a post-training recipe has changed more in the last year than in the prior three. * 2022–2023 (InstructGPT): one pipeline — SFT → reward model → RL. * 2024 (Llama 3, Tülu 3, etc.): open recipes formalize SFT → DPO → RL with verifiable rewards. Closed recipes use many stages of RLHF. * 2025 (DeepSeek R1): reasoning RL (R1) makes large-scale RL the centerpiece. * 2026 (MiMo Flash V2): recipes fragment into many specialist models that are merged back into one. The new thing: MOPD Multi-teacher On-Policy Distillation (MOPD) is the pattern showing up across the 2026 frontier. * Train N domain-specialist teachers (each: SFT, then RL on the relevant domains). * Train one general student by sampling its own trajectories (this is the final post-trained model). * On each rollout, minimize reverse-KL to the relevant teacher’s output distribution, token by token. Lineage: MiMo Flash v2 introduced it → DeepSeek V4 & Nemotron 3 Ultra scale it to >10 teachers. Why did MOPD emerge? * RL got expensive and conflict-prone. Mixing math, code, and agentic RL in one run eventually trades capabilities off against each other. * Specialists are cheap to make / organizationally scalable. SFT-then-RL on a single domain is well understood and parallelizable. As post-training becomes more complex, scaling it across organizations is a big win. * On-policy distillation matured. Literature and know-how continued to emerge through the RLVR renaissance. Sources: DeepSeek V4 §5.1, MiMo-V2-Flash Key historical recipes InstructGPT (Mar. 2022) — the canonical 3 steps · paper * SFT on human demonstrations * Reward model trained on human comparisons * PPO against the reward model Llama 2 (Jul. 2023) — multi-stage RLHF · paper · interconnects recap * SFT, then iterative RLHF over multiple rounds * Each round: rejection sampling → PPO * Two reward models — separate helpfulness and safety Llama 3 (Jul. 2024) — a complex multi-stage recipe with simpler optimizers · paper · interconnects recap * Per round: reward model → sample K per prompt → rejection sampling → SFT → DPO * No online RL — the RM only filters; run over 6 rounds, best models seed the next Tülu 3 (Nov. 2024) — simple three-stage post-training · paper · interconnects recap Curated prompts → SFT → DPO → RLVR (RL with verifiable rewards — the acronym was coined in this paper). OLMo 3 (Dec. 2025) — a reasoning update to the Tülu 3 recipe · paper · interconnects recap DeepSeek R1 (Jan. 2025) — RL as the centerpiece · paper · inte
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This has been a week where the balance of American open-weight models took a step change back toward the frontier. Meta took the lead of this charge with Muse Spark Open, which is now the strongest American-made open model by quite some margin, but the addition of Reflection’s first model Beam is also relevant. Without these two models, there would be 20+ open-weight models built and released by Chinese companies better than the best Western models. At the time of writing this, Mistral has also announced a strong model, Mistral Large 4, with an index score of 38 (see below), but its weights are not yet released. There are rumors of a few more strong American open-weight models coming from other organizations too! In many ways, the gap between the best American and Chinese models has been reduced roughly by half. We’ve gone from the best American open models being largely irrelevant to consumers who require the cutting edge of performance, to them being behind but relevant. This paints the picture of the cut-throat, competitive nature of the frontier model business. Reflection and Mistral announced their model scores and Meta dropped the weights of a smarter model before these other two labs could get their own weights out. All of these models are competing with Chinese models released months ago, where the industry is collectively asking — where have Kimi and Zhipu AI been since the releases of GLM-5.3 and Kimi K3 on Aug. 18th and Jul. 16th, respectively? That is a very nuanced question, which I’ll dig into another time. Today’s piece is focused on Meta’s turnaround, what Muse Spark Open means for the ecosystem, and the balance of power across Western frontier labs. Why Muse Spark Open should take off — and why it may not One thing we see again and again at Interconnects through our open model analysis is that Western open-weight models get astonishingly more adoption than Chinese models with the same performance-to-size footprint (even though the Chinese models usually come out first)! Muse Spark Open is landing into this environment, at the same time as when open-ended LLM agents are finally real (~GLM 5.2 capabilities and on). Riding off a rapid increase in performance of Chinese models, the open ecosystem has been living through an acceleration in demand. The inference providers are rapidly scaling, the open post-training economy is forming, and countless enterprises are looking to own their intelligence. Muse Spark Open’s performance falls somewhere above GLM 5.2, near DeepSeek V4.1 Flash, and behind GLM 5.3 or Kimi K3. It should be squarely “good enough” to slot right into this. For some background, from the Muse Spark Open model card on Hugging Face: Muse Spark Open is an 816B total/42B active parameter open weights model with a context window of 1M tokens. Built for coding and agentic work, Muse Spark Open is natively multimodal. This open weights version has additional safety post-training to make it more robust against misuse as a standalone model without system guardrails. We recommend deploying Muse Spark Open as part of a broader AI system, with additional guardrails suited to the specific use case. Here’s the distribution of the various Muse models on the Artificial Analysis Intelligence Index. This is included to loop back to the original model card snippet. It was shared that this open-weight model underwent additional safety training to prepare for this open release. Many observers saw this release as potentially a model that was “broken” during the safety training, as it came out after Muse Spark 1.3. In reality, this was much more likely a safety tune of Muse Spark 1.2, which would only be a 1 point drop on Artificial Analysis to shore up safety behaviors. When Meta released Muse Glimmer, the 30B agentic model, Alex Wang said that they were preparing to release the Muse Spark 1.2 model as open weights, which fits with the above. If anything will hold Muse Spark Open back, it’ll be the specific license they
I’ve had one too many discussions on the cyber risks of open models — where someone assumes that either banning open models happens in a silo (and bad actors will somehow actually be stopped) or that China is a threat and doesn’t care about AI safety — that I feel we’re going to end up making policy decisions that both limit American AI competitiveness and increases long-term cyber risk. I feel like we’re locked in on a path of lose-lose situation, unless people embrace nuance, trade-offs, and scary realities. Much has been written about the views of various players in this debate, which I summarize as follows: * Open weight models pose an untenable risk to society’s functionality, occupied by frontier lab leadership and the national security community in the U.S. * Western voices saying open weight models are necessary for defense and banning them will make the world less safe, occupied by AI risk moderates to different extremes. I include myself in this bucket, along with Hugging Face’s perspective after the OpenAI incident, and others like Joshua Saxe. * Chinese companies continuing to release open-weight models with strong cyber capabilities, who are making decisions based on the risk assessments of their society and government. This list is also organized by volume or clarity of message in the Western AI ecosystem. The risk-focused discussions have been disproportionately visible, including pieces of work which I view as very detrimental to potential of engaging across assumed party lines on this issue. There’s also very little substantive engagement trying to understand how Chinese companies assess risk — rather, mostly arguments that border on ad hominem attacks like “Chinese labs don’t care about safety.” Interconnects AI is a reader-supported publication. Consider becoming a subscriber.
This episode is with Jean-Stanislas “JS” Denain of Epoch AI, who leads their Insights Team and is one of the people I find myself debating the state and trajectory of AI with more and more. We’ve had follow-on discussions of many of my favorite recent posts online and/or in private, so I wanted to dig into the nuance in a public episode. A big takeaway of this podcast is how JS and I both have so much uncertainty with exactly where we are heading, and this was our best effort at stating our observations today. Chapters / topics include: * 00:00 Predictions for RSI * 18:15 The role of robotics in an AI acceleration * 24:20 How far behind are Chinese models? * 27:39 Does distillation explain the gap? * 40:58 What Chinese job postings reveal about their labs * 48:13 Are open or closed models safer? * 58:10 How Epoch AI ticks * 1:00:55 What a frontier post-training recipe looks like Enjoy! More from JS: Epoch AI profile and writing, X, LinkedIn Listen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here. Transcript 00:00:00 Nathan Lambert: I’m here with JS Denain, who is a senior researcher at Epoch AI. He leads the insights team. He is one of the people who I feel like I get the best feedback on my writing from, whether it’s from US-China AI capabilities, now RSI. And I just wanted to open this discussion and honestly go deeper with him, trying to understand how he thinks about these various things. And I think you have a very useful, moderate point of view, which I feel like you’re probably a step further into what would be called faster scenarios for AI progress. But let’s get into this, and it’s like, what measurements do you think OpenAI and Anthropic are seeing when we get all these proclamations on RSI happening very imminently? 00:00:47 JS Denain: Yeah. I think, so there’s the measurements they’ve published, right? So, OpenAI and Anthropic both had blog posts, I mean, Anthropic two at least, on the effect AI has on accelerating AI progress. I think at least the things they publish, I don’t think are super strong evidence of imminent self-sustaining acceleration AI capabilities, or full automation of the job of AI researcher. But I think the kinds of things we see are, I think probably the most striking thing I saw in the OpenAI blog post was increasing usage of AI systems in model deployment, like the increase in spending on Codex that we saw. And it’s kind of unclear how exactly to interpret this, because maybe it’s a measurement artifact where they’re only looking at Codex, but in fact, there was a bunch of ChatGPT usage before from the researchers. But overall, that plot, for example, just shows a 2X a month increase in Codex spending by researchers, and that does seem to me to be some evidence of they’re getting a lot of value out of this probably. I don’t think this is strong evidence that in six months we have a software intelligence explosion. 00:01:57 Nathan Lambert: Do you think this is the same? So, what is the information they have internally relative to what we have? And this is obviously hypothetical. We don’t have this internal information. Because I get the sense that a lot of people are more scared in their updates from the labs than the information we have. And I try to take this very seriously of, what will they be seeing that is making the acceleration of risk comments go faster, and how much of this is material evidence versus how cultures evolve over time? And I’m much more interested in evidence. 00:02:29 JS Denain: Yeah. So two things. I think, first of all, I guess I don’t think, I don’t know, right? I don’t have full information here. I don’t currently think that either there’s some specific thing that people at OpenAI or Anthropic are seeing right now that we don’t have access to that warrants being way more freaked out about this. I also don’t think that... I think the public evidence we have right now, more general on AI progress and just
I was recently invited to brief a group of Congressional members and staff on the state of open-weight models in the lens of U.S.-China competition. I’m sharing my prepared remarks as a state of the union on open models that is accessible to a broader audience. Interconnects AI is a reader-supported publication. Consider becoming a subscriber.
We’re in an era where a few organizations are using thousands of concurrent agents to improve their processes and output. These organizations happen to be just the frontier AI labs, in particular OpenAI and Anthropic. In the last few weeks, I’ve been pondering what it means for so many employees across these organizations to rapidly update their expectations for the pace of AI progress and associated risks. A core perspective I have is that the frontier labs and broader frenetic, competitive culture in the San Francisco AI scene set up an environment that amplifies any AI concern. This has some benefits in causing more general audience awareness of AI, as fear sells, but exaggerating risk timelines or severity will have negative second-order effects. I remember many loud AI safety debates, and their associated clouds over the viability of open-source AI, in 2023 and 2024 — the primary risks then did not arrive in the forecasted timelines. The general populace of these two key labs was very anxious about AI risks and the rate of progress even a year ago, and especially as agents got stronger product-market fit at the start of 2026. This cultural precondition, when exposed to the reality that thousands of agents will constantly be working fairly productively in your business, will only increase this anxiety. The step from this anxiety, and incidents like OpenAI-HuggingFace, to extinction risks feels very religious. Richard Ngo had an apt summary of the situation: Now a large proportion of the AI safety community is implicitly or explicitly orienting to futures where an intelligence explosion occurs within a few years. My default expectation (absent an extensive pause) is that a similar thing will happen: they’ll turn out to be directionally correct (relative to the expectations of almost anyone not linked to the community) but factually wrong. Specifically, we won’t have superintelligence within the next 8 years, but things will still be moving so fast that it’ll *feel* like the people who argued for short timelines were right. … I wanted to say something now because it feels like the level of bandwagoning towards “singularity soon” is getting pretty wild. Personally, I think this view aligns closely to what I outlined in my alternate scenario to true recursive self-improvement (RSI), which I called lossy self-improvement. A summary of this view is that: * Automatable research is too narrow to achieve a massive net acceleration in progress, in the face of scaling laws’ exponential costs, * Diminishing returns of more AI agents in parallel are real, & * Resource bottlenecks and politics are a major factor in building strong LLMs (and AI can do much less to accelerate this). So, I’m left balancing the above, latent increase in the cultural temperature with the potential that the labs have seen genuinely scary, specific breakthroughs that are not public yet. My expectation is that more of the current AI safety concern is on the former – scaled agents working – but I hold high levels of uncertainty here. Foundational, imagination-based AI breakthroughs are the sort of thing that would make me update my RSI timelines from closer to a tool to sustain progress in the face of exponential costs (scaling laws), to something more unpredictable and/or unstable. Interconnects AI is a reader-supported publication. Consider becoming a subscriber.