Frozen models improve without weight updates: TextualRL turns repeated same-task attempts into context patches, gaining up to 24.4 points on a six-benchmark average
TextualRL critiques a group of rollouts from the same task under the same context as a unit, rather than an isolated trajectory, and writes reviewed textual patches back into the shared context while model parameters stay frozen. Across five target models and six benchmarks it reaches the highest six-benchmark average for every target, gaining 11.4 to 24.4 percentage points over the no-skill baseline and 0.9 to 3.2 points over the strongest baseline, SkillOpt.