RLVR-TTT

Research combining test-time weight updates with verifiable-reward training, so a model can learn mid-conversation without absorbing wrong information.

Test-time training lets a model update its own weights from context during inference — but on its own it can't distinguish correct information from incorrect information, so it learns whatever it's told. This project explores pairing test-time training with reinforcement learning from verifiable rewards, so a weight update is only kept when it measurably improves performance on automatically generated, verifiable questions about the conversation. Currently in the foundations and literature-review phase ahead of implementation.