Papers with credit assignment problem
Attention Flows are Shapley Value Explanations (2021.acl-short)
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| Challenge: | Shapley Values are a popular type of explanation in machine learning, but leave-one-out and attention-based explanations still predominate in NLP. |
| Approach: | They propose to use attention flow to explain the importance of features, embeddings, and even neurons to explain credit assignment problems in cooperative game theory. |
| Outcome: | The proposed explanations can explain the importance of features, embeddings, and even neurons, but in NLP, leave-one-out and attention-based explanations still predominate. |
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction (P18-1)
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| Challenge: | equivalence between credit assignment problem and entropy regularized reinforcement learning is established . a wide range of successful sequence prediction algorithms have been developed . |
| Approach: | They propose to extend credit assignment in reward augmented maximum likelihood learning by credit assignment and entropy regularization. |
| Outcome: | The proposed algorithms outperform RAML and Actor-Critic on two benchmark datasets. |
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)
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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong
| Challenge: | Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding. |
| Approach: | They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. |
| Outcome: | The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet. |
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (2026.findings-acl)
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Yueyang Cang, Xiaoteng Zhang, Erlu Zhao, Zehua Ji, Yuhang Liu, Yuchen He, Zhiyuan Ning, Chen Yijun, Wenge Que, Li Shi
| Challenge: | Recent approaches to optimize communication topology rely on single-sample policy gradients with absolute rewards. |
| Approach: | They propose a topology optimization framework that integrates Group Relative Policy Optimization. |
| Outcome: | The proposed topology optimization framework outperforms state-of-the-art methods on reasoning and code generation benchmarks. |