ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
Score
76提出了新的答案回溯信用分配机制，为长程搜索智能体训练提供差异化步骤监督，思路新颖且实验支持充分。
方法描述清晰，实验设计较完整，包含多基准和规模对比，但基座模型与数据细节略显简略。
仅用 8.5k 样本与小模型即可达到接近大模型效果，对资源有限的团队具有较强参考意义。
论文发布于近期，且针对搜索智能体这一当前热点方向，时效性强。
来自 arXiv AI/CL/LG 预印本，虽未经同行评审，但作者机构与方法细节可信度较高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper introduces a novel answer-backtracked step-level credit assignment mechanism, with experiments showing significant gains over same-scale baselines and near parity with larger models, offering important insights for agent training.
AI commentary
「What stands out here is moving search-agent training from whole-trajectory rewards to step-level credit assignment, especially identifying useful steps inside failed trajectories. For teams working on agent reasoning alignment or trying to make small models handle complex search tasks cheaply, this clue-backtracking approach is practical and worth adapting to their own pipelines.」
Quick overview
Long-horizon search agents must perform multiple sequential actions to search, retrieve, verify, and integrate evidence. However, existing training methods treat all steps in a trajectory uniformly during both supervised fine-tuning (SFT) and reinforcement learning (RL), failing to distinguish useful actions from erroneous or redundant ones. This paper proposes Answer-Backtracked Credit Assignment (ABC), a fine-grained credit assignment framework that converts sparse trajectory-level outcomes into dense step-level supervision, rewarding useful actions even in failed trajectories while suppressing erroneous or redundant ones. ABC first performs Answer-Backtracked Clue Recovery, tracing backward from the answer to recover intermediate clues needed to solve the question. It then applies Clue-Anchored Step Scoring to evaluate each search step against these clues. Based on these rewards, the authors develop ABC-SFT and ABC-GRPO, and train ABSeeker on Qwen3.5-4B with only 8.5k examples. ABSeeker achieves 37.3% on BrowseComp and 39.1% on BrowseComp-ZH. With context management, scores improve to 55.3% and 52.9%, significantly outperforming same-scale (4B) agents and even matching larger models around 30B. These results demonstrate the effectiveness of answer-backtracked step-level credit assignment for training long-horizon search agents.
- The ABC framework converts sparse trajectory-level outcomes into dense step-level rewards, distinguishing useful, erroneous, and redundant actions.
- ABC-SFT reweights per-turn losses and ABC-GRPO uses step-level scores as rewards, both based on answer-backtracked clues.
- Trained on only 8.5k examples with a 4B model, ABSeeker reaches 37.3% on BrowseComp (55.3% with context management), approaching 30B-scale models.
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