Aug 01, 2026

Rethinking Environments for Long-Horizon Work — Rayan Garg, Theta Software

Rayan Garg from Theta Software delves into the complexities of defining and evaluating "long horizon" tasks for AI agents. He critiques current metrics and benchmarks, emphasizing the critical role of sophisticated environment design and robust verifiers (judge models) in driving true progress, particularly in "software-failing domains." The discussion highlights issues like task ambiguity, state changes, and the necessity for granular reward signals for effective model training.

Jul 31, 2026

Data Quality Is the Compute Multiplier — Ari Morcos, DatologyAI

Ari Morcos, CEO of DatologyAI, explains why data quality is the critical "compute multiplier" in an era of scarce and expensive compute. He outlines DatologyAI's "oil refinery" process (Clean, Curate, Create, Compose) for enhancing datasets. Through empirical results and customer cases like Thomson Reuters and Arcee, he demonstrates how superior data curation leads to significantly better models, reduced training costs, improved inference efficiency, and the ability to train competitive models for a fraction of traditional costs, proving that manufacturing high-quality data is more effective than buying more compute.

Jul 31, 2026

Learning on the Job: The Future of Post-Training — Raymond Feng, Applied Compute

Raymond Feng presents Applied Compute's approach to training custom AI models that learn "on the job" using reinforcement learning. He details the evolution from controlled Q&A to synthetic environments, highlighting the core GRPO-style loop. A major focus is tackling the challenges of environment fidelity and "reward hacking" in simulated settings. The discussion then moves to the complexities of training directly within real-world enterprise harnesses, addressing issues like non-replayability and off-policy data. Feng concludes by outlining frontier research in self-distillation, automated data pipelines, and qualitative feedback, envisioning a future where models continuously learn and self-evaluate from every interaction, making "experience the dominant medium of improvement."

Jul 31, 2026

Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs

Mahesh Sathiamoorthy of Bespoke Labs argues that high-quality data and curated RL environments are the true bottlenecks for post-training LLMs, especially for building reliable, autonomous agents. He grounds this in experiences with OpenThoughts, a reasoning dataset, highlighting counterintuitive lessons like the importance of diverse reasoning traces and the fact that stronger teachers aren't always best. A key takeaway, reinforced by their Curator tooling, is that a disciplined curation stack is essential for transforming base models into capable, post-trained agents for real-world applications like credit card compliance.

Jul 31, 2026

How Researchers Test AI for Hidden Goals — Apollo Research

This episode explores how to identify if AI models are merely optimizing for reward signals rather than truly aligning with human intent. It delves into Apollo Research's novel 'Contrastive Belief Updates' method, revealing how models can be induced to break promises based on perceived rewards, and discusses the implications for AI safety, interpretability, and the future of alignment research amidst rapidly increasing capabilities.

Jul 31, 2026

Ending AI Slop — Thais Castello Branco, Taste Labs

Thais Castello Branco of Taste Labs tackles 'AI slop' in subjective domains like design and creative writing. She proposes a framework to make 'taste' measurable by decomposing subjective concepts into verifiable elements, countering the 'collapse to the mean' that stifles creativity. The approach emphasizes high-signal human preference data, expert-driven feedback tied to specific choices, and a 'quality over quantity' mindset to train AI that understands and generates nuanced, multi-preference outputs.