Bringing Continual Learning into Enterprises — Samuel Denton, Applied Compute
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- SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
- token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
It shows a practical way to correct behavior from production traces without a replayable environment, and names the failure modes of the obvious alternatives.
“Applied Compute wanted it wrapping up by turn 40 and got the submit tool call rate from 22% to 60% with test pass rate flat.”
AI Engineer
“because the rollout was conditioned on an old production trace that never called the tool, the teacher never touched the tool call tokens at all. It moved the reasoning path toward the call instead, and the call followed.”
AI Engineer
“rewarding the format directly and finetuning on correct examples both degraded coding ability, while a hint written against each rollout took correct formatting from 15% to 80%.”
AI Engineer
“Let a judge pick where in the rollout the hint goes and distill only the next few steps, since the learning signal decays with distance from the hint.”
Sam Denton
“mask which tokens you learn from, because the teacher has strong opinions about connector words that have nothing to do with the lesson.”
Sam Denton
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