Agent Alignment

In the race to make Large Language Models (LLMs) reason like humans—or better—most researchers obsess over one thing: prompting. Chain-of-thoughts, few-shot demos, scratchpads, tools. But a new study from NVIDIA suggests something even more fundamental: it’s not just how you prompt them—it’s how long you train them. Their paper, Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training, explores how stretching reinforcement learning (RL) over time unlocks broader, more stable, and more versatile reasoning in LLMs. This isn’t just about incremental gains—it’s about escaping reasoning ruts. ...

🧠 From Freud to Fine-Tuning: What is a Superego for AI? As AI agents gain the ability to plan, act, and adapt in open-ended environments, ensuring they behave in accordance with human expectations becomes an urgent challenge. Traditional approaches like Reinforcement Learning from Human Feedback (RLHF) or static safety filters offer partial solutions, but they falter in complex, multi-jurisdictional, or evolving ethical contexts. Enter the idea of a Superego layer—not a psychoanalytical metaphor, but a modular, programmable conscience that governs AI behavior. Proposed by Nell Watson et al., this approach frames moral reasoning and legal compliance not as traits baked into the LLM itself, but as a runtime overlay—a supervisory mechanism that monitors, evaluates, and modulates outputs according to a predefined value system. ...