Learning to Solve Hard Problems in RL for LLMs by Never Giving Up
Summary
A blog post detailing a RL post-training approach for LLMs, introducing the Matthew Effect in RL for LLMs and a method called Never Give Up (NGU) to allocate compute more efficiently. It analyzes eval signals, scalability to math and code tasks, and limitations, arguing that RL improvements skew toward easier problems and proposing adaptive NGU to tackle harder ones.