Definition
Plain language
A reinforcement-learning trick that keeps generating tries on a problem until you have a useful mix of wins and losses.
As stated in the literature
Balanced Adaptive Rollout, an RL rollout strategy that adaptively expands group size per prompt until the success-failure mix produces a nontrivial gradient signal.
Also called: Balanced Adaptive Rollout
Why it matters: In RL with verifiable rewards, all-success or all-failure groups produce no gradient — BAR is a practical fix that keeps training signal alive on hard problems.
For example, if the first eight tries on a hard problem all fail, BAR keeps sampling more rollouts until at least one succeeds and one fails, so the learning signal isn't all zeros.
Heard on the show
“It's called Balanced Adaptive Rollout, BAR for short, and it solves a problem that's specific to how RL works for agents.”Episode 047 — When Agent Benchmarks Lie: The Harness Problem in Open-Source AI