Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aims to decompose trajectory-level rewards and provide more fine-grained supervision for intermediate decisions. However, existing credit assignment approaches ignore the rich process information naturally generated during environment interaction, e.g., interaction history. We argue that such information provides valuable supervision for identifying the contribution of individual actions. To this end, we propose Environmental Feedback-based Credit Assignment (EFCA), a multi-timescale credit assignment approach for long-horizon agentic RL. EFCA complements the long-term outcome signal with two environment-grounded process signals: a short-term feedback signal that captures the immediate effect of the current action and a medium-term state-history signal that identifies ineffective patterns from recent interactions. Both signals are directly extracted from environment feedback and integrated through a return reweighting mechanism. Experiments on ALFWorld and WebShop demonstrate that EFCA consistently improves both task success and task quality over strong baselines, highlighting the effectiveness of environment-grounded multi-timescale credit assignment for long-horizon agentic RL.
Yifu Huo, Shunjie Xing, Chenglong Wang et al.· 0 citations
Flow Continuous Trajectory Supervision (FlowCTS), which matches subsequent student and reference trajectories initialized from the same student-visited state to derive a temporally weighted velocity-matching upper bound and discretize it into practical objectives parameterized by the number of supervision steps.
Kaiyang Ye, Yuan Ge, Junxia Zhang et al.· 0 citations
The method Syfer is introduced, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default and attains competitive accuracy while striking a favourable balance between performance and computational cost.
Yilin Wang, Yuchun Fan, Weidong Bao et al.· 0 citations
This work proposes Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning.
Jiaoyang Li, Junhao Ruan, Shengwei Tang et al.· 0 citations
ToFu is presented, an agentic harness for researchers that reads your codebase, edits files, runs commands, and integrates with your development tools and provides a white-box agentic harness that allows researchers to inspect, modify, and evaluate its orchestration logic, tool-use behavior, and harness design.
