The authors propose TrajLong, which reorganizes agent trajectory contexts and constructs questions and target responses around information dependencies.
Compiling agent trajectories into targeted long-context tasks boosts model capabilities
TrajLong organizes trajectory data into supervision for evidence grounding, multi-evidence aggregation, and temporal state tracking, improving performance on both long-context and agent benchmarks.
Chinese Tech
Miao Peng · Qintong Zhang · Nuo Chen · Yuhan Li · Guochen Yan · Xinran Gu · +5 more
Hong Kong University of Science and Technology (Guangzhou) · Tencent · Peking University
Research Digest··2 min read
The authors introduce TrajLong, a framework that compiles LLM agent trajectories into long-context training tasks targeting three atomic capabilities: evidence grounding, cross-evidence aggregation, and temporal state maintenance.
Why this paper
From Tencent and 2 others
In one line
TrajLong compiles agent trajectories into long-context training tasks targeting evidence grounding, cross-evidence aggregation, and temporal state maintenance, improving LLM agent performance.
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