RL Theory Debate: Policy Gradients Converge at KKT Points, Not Reward Targets
neocartesian · x · 2026-09-12
neocartesian responds to "Reward is not the optimization target" with a decision-theoretic analysis of RL:
- Ideal policy gradient optimization is stable at KKT points of the policy-to-expected-reward mapping;
- These KKT points correspond to CDT+GT equilibria of imperfect recall games, with standard perfect-recall RL as a special case where the GT component becomes irrelevant (GRPO noted);
- neocartesian also questions how to reconcile this view with a mental model of RL as objective-directed synthetic data generation, guessing it hinges on whether the reward signal carries enough information for convergence.
More from Research
- kalomaze: You don't need an analytic transfer theory, just a learnable transfer-extrapolation function — kalomaze · 2026-09-12
- kalomaze: Information asymmetry, not verifiability, is the general primitive behind RLVR gains — kalomaze · 2026-09-12
- Simons Institute holds workshop on AI's rapid acceleration of mathematics and theoretical CS — jasondeanlee · 2026-09-12
- Dev Fine-Tuned a 2B LLM on WhatsApp Group Chat, Simulating Six Friends on an M1 Pro — BarisSayit · 2026-09-12
- First quantitative evidence: Claude and GPT now use GUIs as well as APIs — ysu_nlp · 2026-09-12
- World Models Will Power the Next Leap in AI Agents — And They May Never Show Video — furongh · 2026-09-12