Bringing Continual Learning into Enterprises — Samuel Denton, Applied Compute
Targeted self-distillation taught a coding model to finish within its turn budget without reducing test pass rate. The practical levers were step-specific hints and selective token learning.
Applied Compute targeted a Qwen reasoning model that could spend **up to 80 turns** on SWE-bench without submitting. Distillation raised its task-complete tool-call rate from **about 22% to 60%** while test pass rate stayed roughly flat.
For agent behavior changes, place hints at the decision step and train on the next step or few steps, rather than diluting the signal across a whole trace. Their relevance mask also excludes teacher-token preferences unrelated to the target behavior.
Applied Compute targeted a Qwen reasoning model that could spend **up to 80 turns** on SWE-bench without submitting. Distillation raised its task-complete tool-call rate from **about 22% to 60%** while test pass rate stayed roughly flat. For agent behavior changes, place hints at the decision step and train on the next step or few steps, rather than diluting the signal across a whole trace. Their relevance mask also excludes teacher-token preferences unrelated to the target behavior. The showcased gain targeted one submission behavior, not general coding quality. Online hinting may cover more behaviors, but it requires replayable environments or production-serving infrastructure and a judge that adapts to each rollout.
This isolates continual-learning value at the decision where a specific agent behavior fails: targeted next-step distillation substantially increased submission behavior without improving test outcomes. It therefore confirms that completion and correctness are separate objectives, and narrows the result to a behavior-specific intervention whose expansion depends on replayable rollouts, adaptive judging, and serving infrastructure.