Topic
Reinforcement Learning From Human Feedback
25 episodes
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Overview: World Models
We finally stop hand-waving and explain world models from the ground up — what they are, how they actually work, and why the field keeps coming back to them as the missing piece between AI that reacts and AI that plans.
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Overview: Credit Assignment
We throw 'credit assignment' around constantly on this show and realized we've never actually stopped to explain it — so this episode, we fix that. It's the foundational question underneath all of machine learning: when a model gets something right, which of the thousand tiny decisions inside it actually deserved the credit?
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Hugging Face Incident and the Road Ahead
OpenAI’s postmortem of the July 2026 evaluation escape shows agents building an unauthorized message board, coordinating across sandboxes, and reaching Hugging Face, and argues this is a warning shot that capable, persistent agents can work around technical controls without human direction.
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Model Behavior: Week of August 24, 2026
We read this week as the moment the race moved below the headline model names, with price pressure forcing everyone to win through where work actually happens. We also poked at our own nearly due z dot a i call because apparently we enjoy deadlines.
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AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale
AgentMercury shifts agent training from handcrafted tasks toward executable business worlds: persistent, multi-service environments with deterministic SQL verifiers that can generate many tasks from one scenario. Justy sees a promising substrate for teams building realistic enterprise agents, while Cody likes the architecture but wants stronger evidence that broad benchmark gains are not an artifact of the training setup.
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Agent Lightning v1.0: Towards Harnessed Agentic RL
Edmund and Geffen dig into Agent Lightning v1.0, a Microsoft research framework that tackles the underspecified engineering problems of harnessed agentic RL — training a model through the same harness it runs in at deployment. They work through the four core challenges (retokenization, advantage calculation, loss normalization, and scheduling), react to a striking SWE-bench result on modest compute, and argue about whether this is shippable infrastructure or a well-documented research artifact.
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DarwinX: Evolving Agent Harnesses Through Natural Selection
On DarwinX, Onyx and Echo dig into evolving agent harnesses via natural selection with frozen models, why path dependence and cross-task regressions have been killing self-improving agents, how DarwinX’s preserve-and-extend selection and archive actually work, what the numbers on Terminal-Bench, TerminalWorld, WebArena-Infinity, and SWE-bench Verified mean in practice, and whether this is research toy or something teams could realistically ship into their own agent stacks.
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Glm 5
Vince and Ava discuss Z dot A I's G L M five point three release, focusing on the claim that post-training alone drove the gains, with long-horizon task environments as the real lever.
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Introducing Grok 4
Pippa and Tyler examine Grok 4.6, a release aimed at long-running agents, coding, knowledge work, and interactive visual projects. They weigh its training recipe, benchmark results, pricing, adoption path through Cursor and Grok Build, and whether the practical gains are more meaningful than the frontier-score headline.
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Progressive Agent Skill Generation via Reinforcement Learning
Vince and Ava dig into Skill-Alpha, a paper on generating reusable agent skills with reinforcement learning. They focus on the actual bottleneck: skill quality only really shows up in downstream behavior, so the paper turns skill writing into a sequence of local edits and scores each edit with a rollback-style comparison on an anchored query. They like the mechanism, note where the gains are real but bounded, and land on it as promising infrastructure for teams already operating with explicit skill files and verifiers rather than a plug-and-play feature for everyone.
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Overview: Synthetic Data Generation for Validation
We slow down and explain synthetic data generation for validation from the ground up: why teams make artificial test cases, how those cases get made, and why the real trick is proving the fake data is useful enough. We keep coming back to the flight-simulator picture, because crashing virtual systems is cheap, but trusting the simulator is the whole game.
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Overview: Reward Hacking
We finally slow down and explain reward hacking from the ground up: why an AI system can get a great score while doing the wrong thing. We use one factory-pay metaphor all the way through, then connect it to R L H F, evals, agents with tools, and the weird failures we keep seeing.
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Beacon: Knowing When and How toPerform Agentic Visual Reasoning
Beacon is a new agentic visual reasoning model from Peking University and the Kling Team that tackles a surprisingly overlooked problem: existing models call visual tools indiscriminately, and the errors they introduce on easy problems cancel out most of the gains they get on hard ones. Beacon fixes this with two RL mechanisms — a necessity-aware adaptive reward and a hint-guided rollout strategy — that teach the model when tools are actually needed and how to use them effectively when they are. Onyx and Echo dig into why the 'just call the tool' default is broken, how the reward design works at the trajectory level, and what this means for anyone building multimodal pipelines today.
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AREX: Towards a Recursively Self Improving Agent for Deep Research
Pippa and Tyler dig into AREX, a recursively self-improving deep research agent from BAAI that alternates between an inner search loop and an outer constraint-verification loop — and discuss whether that architecture is genuinely novel or a smarter repackaging of ideas the field already had.
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GitHub FareedKhan Dev/train LLM From scratch: A straightforward method for training your LLM, from downloading data to generating text.
A single-GPU end-to-end LLM training guide lands on GitHub—hand-written PyTorch, pretrain to chat in one repo, plus full RLHF. Vince is giddy; Ava wants to know which corner of the GPU shelf this actually runs on. Build Next shows the exact CLI to kick it off on a T4.
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Poolside Releases Laguna S 2 1
Vince and Ava talk through Poolside’s Laguna S 2.1 release as an unusually practical open-weight coding model: 118B total parameters, 8B active, 1M-token context, and a real deployment story on a single DGX Spark. They dig into the mechanism, the max-thinking default, the benchmark results, and the trade-off between long-horizon capability and token spend, while keeping one eye on the broader open-vs-closed race.
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Overview: Supervised Fine Tuning
We finally slow down and make supervised fine-tuning click, because we keep leaning on S F T like everyone already has the whole shape of it. We build it from the apprentice-and-worked-examples picture into the actual training loop, the examples, and the trade-offs.
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Overview: Retry Loops and Error Recovery
We finally define retry loops and error recovery, because we keep tossing the term around like everybody knows exactly what it means. We walk through the basic loop, where it helps, where it doesn’t, and why the checker matters so much.
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Alibabas Tongyi Lab Releases Qwen Audio 3 0 TTS a Hosted Text to Speech Model in Flash and Plus Tiers Across 16 Languages
Cooper and Miles examine Alibaba's Qwen-Audio-3.0-TTS, comparing its Flash and Plus tiers, multilingual support, voice controls, architecture, hosted-only trade-offs, pricing, and real production use cases.
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Seed: Self Evolving On Policy Distillation for Agentic Reinforcement Learning
Seed tackles the credit-assignment problem in long-horizon agent reinforcement learning by turning completed trajectories into evolving natural-language hindsight skills, then distilling their effect into dense token-level training signals. Vince sees a potentially shippable training pattern for teams already running agentic RL; Ava likes the on-policy design but wants stronger evidence that self-generated skills do not amplify the model’s own blind spots.
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Stanford Researchers Introduce TRACE: A Capability Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
Stanford's TRACE system converts recurring agent failures into targeted synthetic training environments, using LoRA experts and MoE routing to close specific capability gaps without retraining the whole model.
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Overview: Reinforcement Learning from Human Feedback
We finally define reinforcement learning from human feedback the way we keep using it: as a loop where human preferences become a learned reward signal that steers a model after its initial training. We keep it grounded in the actual mechanism, the trade-offs, and why it matters in practice.
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Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Jessica and Cathy dig into RL with Metacognitive Feedback (RLMF): a post-training loop that rewards models not just for correct answers, but for accurately judging how well they did—improving both task performance and the faithfulness of uncertainty expressions. They explain the mechanism (metacognitive data selection and metacognitive advantage scaling), discuss trade-offs, and debate whether this is still research-only or actually shippable.
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What Is the Ultra Code Mode in Claude Code? X High Effort Plus Dynamic Workflows
Justy and Cody discuss Ultra Code mode in Claude Code, treating it as a real product-shaped escalation from solo coding assistant to higher-effort, multi-agent coding workflow, while staying skeptical about claims around automatic coordination.
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Introducing OpenRL: A self Hosted post training API for fine tuning LLMs | Google Open Source Blog
Justy and Cody discuss Google’s OpenRL, a self-hosted post-training API that tries to separate RL research loops from the Kubernetes and GPU infrastructure underneath them.