The authors constructed corpus-grounded questions by composing relations while retaining intermediate answers, supporting passages and dependencies between pieces of evidence.
Local retrieval credit helps search agents learn multi-step evidence gathering
Experiments with a 4-billion-parameter model show that rewarding the specific search action that finds useful evidence improves question-answering performance.
Chinese Tech
Wenyu Huang · Xinyu Hou · Pavlos Vougiouklis · Ruofei Lai · Jeff Z. Pan
University of Edinburgh · Huawei Technologies Research & Development (UK) Limited
Research Digest··2 min read
Huang and colleagues examine how intermediate retrieval feedback should be incorporated when training search agents with reinforcement learning.
Why this paper
From Huawei Technologies Research & Development (UK) Limited and University of Edinburgh
In one line
Local intermediate retrieval rewards improve search agent training more effectively than outcome-only reinforcement learning.
What we could check
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- ✓Reports numbers on named benchmarks (2 benchmarks)
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