Can agents learn from their own actions without external rewards?
Explores whether future states produced by an agent's own decisions can serve as supervision signals, bridging the gap between passive imitation learning and reward-dependent reinforcement learning.
Can agents learn beyond what their training data shows? Can agent deployment itself generate training signals automatically? Can scalar rewards capture all the information in agent feedback? Can transformers learn to solve new problems within episodes? Can agents learn from failure without updating their weights?
Expert demonstrations bound agent learning to curator imagination Next-state signals from deployment unify all agent training under one loop Agent feedback decomposes into evaluative and directive signals scalar rewards cannot jointly capture RL-finetuned transformers develop in-context reinforcement learning from episode experience Agents learn from binary feedback by storing verbal reflections in episodic memory
Does AI passivity explain why coaching feels more helpful than execution? Can explicit goal state scaffolding at inference time transfer to autonomous tracking through training? Why does online RL succeed where supervised training fails for self-correction? Can AI systems execute strategies without conscious intention behind them? What capabilities can emerge from self-modification that the original agent lacked? What information do next-state signals contain beyond what scalar rewards capture? How do implicit world models and self-reflection operationalize consequence-based learning? Can next-state supervision work across different agent interaction types like conversations and tool calls? What happens when agents interact with environments and learn from their own mistakes? What makes process-level supervision better than outcome-only reward signals? Can subjective tasks be delegated without human feedback loops? Can cooperative AI systems make meaningful decisions without a stable self?See all 36 inquiring lines on this note →