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.
Related event: Researchers Debate Whether Reward Is the Optimization Target in Deep RL(9 posts)→
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