The paper introduces Grounded Credit Assignment (GCA) for reinforcement learning in multi‑turn search agents. GCA separates credit for evidence retrieval and answer correctness, correcting the bias in GRPO that favors evidence‑retrieving rollouts only proportionally to accuracy gains. Applying GCA to Search‑R1 boosts average EM by 5.7–6.3 points and increases the proportion of evidence‑supported correct answers from 76.9% to 93.4%.
By Qirui Liu, Hao Chen, Yi Wen, Weijie Shi, Yao Zhao, Xianhe Chen, Hao Zhao, Xing Tang, Weihong Luo, Yuyu Luo
BiCAA introduces bidirectional credit assignment for search-augmented agents by combining Forward Solvability Gain (FSG) and Subsequent Reasoning Gain (SRG). FSG measures how each search step increases the likelihood of the correct answer, while SRG evaluates how a step supports future reasoning, discouraging redundant retrieval. Experiments on seven open‑domain QA benchmarks and two Qwen backbones show that BiCAA improves average F1, especially on multi‑hop tasks.
By Yibin Huang, Bin Xu, Hailong Cao, Conghui Zhu
arXiv:2610.07349v1 Announce Type: cross
Abstract: Group Relative Policy Optimization (GRPO) avoids a separate critic by estimating advantages from rollout groups. For multi-turn agents, however, traj...
By Sayak Chakrabarti, Sathish Reddy Indurthi
Self-Credit is an agentic reinforcement learning method that transforms sparse outcome rewards into dense supervision signals through self-generated credit assignment and intra-group rescaling. The approach involves the policy model summarizing key evidence with citations, refining these citations by comparing actions across successful and failed rollouts, and reweighting advantages based on the rescaled credit. Experiments on BrowseComp-Plus, GAIA, and xBench-DeepSearch show significant performance gains over baseline models and other agentic RL methods.
By Fangcong Yin, Ming Yin, Tanvi Bhandarkar, Aashu Singh, Eryk Helenowski, Shlok Kumar Mishra, Saghar Hosseini, Karthik Abinav Sankararaman, Greg Durrett, Dinkar Juyal
arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.
By Xingyu Guo, Wei Chen, Linlin Yang, Baochang Zhang
arXiv:2605. 12969v3 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) is one of the most widely adopted RLVR algorithms for post-training large language models on reasoning tasks.
By Feng Zhang, Xinhong Ma, Ziqiang Dong, Xi Leng, Jianfei Zhao, Xin Sun, Yang Yang, Guanjun Jiang
The paper introduces Distill Until Divergence, a method for agentic reinforcement learning that uses error-truncated hindsight distillation. It routes two complementary hindsight skills—Positive for successful workflows and Negative for failure corrections—based on trajectory outcomes and error boundaries. The post-error segment is optimized solely by GRPO, preserving outcome-based learning while avoiding mismatched hindsight supervision.
By Yibin Huang, Conghui Zhu
arXiv:2609.36178v1 Announce Type: cross
Abstract: Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment o...
By Dongwon Jung, Hemanth Neelgund Ramesh, Yifan Wang, Xiaomin Li, Yuexing Hao, Yu Hu, Muhao Chen, Varun Chandrasekaran, Andrzej Banburski-Fahey, Jaron Lanier
The paper introduces RECAP, a redundancy-aware credit assignment method that improves reasoning efficiency in large language models by assigning credit to each reasoning step based on its downstream role and contribution to the correct answer. RECAP uses a semantic dependency graph to measure structural responsibility and evaluates step efficacy via changes in gold-answer log-likelihood, enabling step-specific updates without requiring a separate reward model or concise trajectories. Experiments on two 7B models across four mathematical reasoning benchmarks show that RECAP enhances the accuracy-efficiency trade-off, boosting pass@1 by 2.0–3.7 percentage points while cutting reasoning tokens by 8–31% compared to GRPO.
By Yuqing Zhou, Hong Wang, Manqing Mao, Zhuoer Wang, Samson Koelle, Jie Yuan, Yanjun Lin, James Feng, Nikki Lijing Kuang, Ziwei Zhu, Wei Niu
TreeHCA introduces a method that couples hindsight-guided exploration with hindsight credit assignment for multi-turn large language model agents. By using changes in reference-answer likelihood to revisit intermediate histories and sample alternative continuations, it builds a tree of reasoning and tool interactions, and applies an inverse-hindsight importance correction to propagate terminal outcomes into turn-level advantages. Experiments on seven question-answering benchmarks with three Qwen backbones show that TreeHCA outperforms all baselines in aggregate exact match.
By Debajoy Mukherjee, Jeremy Carleton, Sushil Vemuri, Srinivas Shakkottai, Dileep Kalathil
arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.
By Huan Zhang, Mingju Chen, Dongxu Zhou, Can Lv, Heng Chang, Sen Cui, Faguo Wu, Shiji Zhou
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approache...