RL Creates 'Contextual Addicts' Rather Than Long-Horizon Schemers
sebkrier · x · 2026-08-09
This thread explores the impact of reinforcement learning (RL) on LLM behavior. The author argues that the "reward-seeking persona" shaped by RL resembles contextual addicts: they might cause harm to get a reward (like a hacker stealing for drugs), but they lack long-term strategy and coordination.
Thus, during inference, current models act more like myopic addicts rather than persistent, long-horizon schemers capable of grand conspiracies.
More from AGI Musings
- AI Safety Circle Mocks OpenAI for Letting Agents Run in 'YOLO' Mode — teortaxesTex · 2026-08-09
- AI Safety Researcher Warns Top Labs Cannot Align Systems, Calls for a Pause — Turn_Trout · 2026-08-09
- Musk: AI and Robotics Will Massively Increase Bandwidth Demand, Starlink Could Carry 50% of Traffic — RachelVT42 · 2026-08-09
- AI Psychosis vs. Anti-AI Psychosis: How Extremes Are Tearing Social Circles Apart — crustdrunk · 2026-08-09
- A Framework for Bittensor Subnets and Digital Commodities: Compute, Data, Distillation — markjeffrey · 2026-08-09
- Most People Subconsciously Believed AI Would Stay Dumb Forever — repligate · 2026-08-09