# SumOne

Long-horizon language-model agents accumulate ever-longer interaction histories, so context compaction is necessary, yet existing learned compaction methods often depend on downstream task outcomes or solver behavior. SumOne is a standalone 2-billion-parameter summarizer trained with a task-agnostic reward built from masked reconstruction and optimized with GRPO, using only raw trajectories and no reference summaries or downstream task execution. On DeepSearchQA it cuts cumulative agent input from 2.83M to 591K tokens with F1 0.48 versus 0.52 for full context; on SWE-bench Verified it reaches a 0.59 resolved rate versus 0.63, with input falling from 4.10M to 1.09M tokens.

Long-horizon language-model agents accumulate ever-longer interaction histories, so context compaction is necessary, yet existing learned compaction methods often depend on downstream task outcomes or solver behavior. SumOne is a standalone 2-billion-parameter summarizer trained with a task-agnostic reward built from masked reconstruction and optimized with GRPO, using only raw trajectories and no reference summaries or downstream task execution. On DeepSearchQA it cuts cumulative agent input from 2.83M to 591K tokens with F1 0.48 versus 0.52 for full context; on SWE-bench Verified it reaches a 0.59 resolved rate versus 0.63, with input falling from 4.10M to 1.09M tokens.