RL
RL on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on rl in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around rl, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Defaulting to Adam without understanding will cost you. Don't "just throw adam at it"
Defaulting to Adam without a foundational understanding can lead to unexpected and frustrating results, particularly in reinforcement learning and deep transformer training. Experienced practitioners have observed erratic loss behavior and instability when applying Adam without careful consideration. This article provides a critical re-examination of Adam's mathematical underpinnings, outlining where it can falter. If you’re navigating the complexities of RL or large-scale models, exploring this analysis is highly recommended—and may prevent a similar experience to /u/Nice-Dragonfly-4823.
Deep Dive on RL and OPD for Training LLMs [D]
Recent advancements in large language model (LLM) training, exemplified by models like Kimi and Qwen, increasingly leverage policy distillation and reinforcement learning from human feedback (RLHF) techniques. To demystify these powerful methods, we’ve published a deep dive exploring the underlying mathematics and code—connecting these algorithms to pretraining and supervised fine-tuning. Discover how RL and OPD are shaping the future of LLMs. Explore the full explanation here: [https://youtu.be/MaZWafi4gYY?is=8jLkAp_Fe86abUVP](https://youtu.be/MaZWafi4gYY?is=8j
Reproducing OpenAI’s “persistently beneficial models” - GRPO trait install barely moves. Ideas? [P] [R]
Reproducing OpenAI’s trait-persistence results presents a significant challenge, particularly at smaller scales. Our attempt to install a "traditionalism" trait (low Openness) using GRPO on Qwen2-7B achieved a minimal improvement of just +2.4 points, falling far short of the ~+15 needed. Despite rigorous debugging—ruling out reward hacking, memorization, and gradient issues—the install remains stubbornly flat. We’re seeking guidance from those with experience in small-scale RLHF/GRPO for trait or persona installation. See "The qlora 2e-4 default is wrong under 10k samples and nobody
Looking for JEPA devil advocates [R]
The emergence of JEPA-like world models presents a compelling, future-focused direction for robot learning, as highlighted by recent research. While Yann LeCun’s vision is undeniably ambitious, a critical evaluation is warranted. We're seeking perspectives that challenge the current trajectory – "devil's advocates" who can identify potential downsides compared to alternative world model approaches. Are there overlooked limitations or vulnerabilities within JEPA’s framework? Explore this discussion, and consider “Are Current AI Memory Architectures Optimizing for the Wrong Abstraction?” for a deeper dive into related challenges.