Building self-improving AI systems

As large language models are applied to complex domains, the frontier is shifting from models trained once to self-improving systems.
Amit will decompose self-improvement into three axes: environment generation, RL training, and harness evolution, including verifiers and memory. For the first two, he will present executable counterfactuals, a framework that operationalizes counterfactual reasoning as code, enabling scalable generation of RL environments with controllable difficulty and verifiable answers. RL training on these environments induces the core behaviors of counterfactual reasoning…

Hosted by Team Lossfunk, Paras Chopra & Jay Joshi