Note Survey of reasoning and agentic RL credit assignment, organized by credit granularity, identification assumptions and evaluation controls. Source: https://arxiv.org/abs/2604.09459v3

Note Combines episode-level relative returns with action comparisons at identical anchor states. Repeated states in collected rollouts supply the step-level comparison groups. Source: https://arxiv.org/html/2505.10978v1#S4.SS2.p2

Note Uses nested groups matching the current state and progressively longer interaction histories. Historical context changes which sampled actions form the credit comparison. Source: https://arxiv.org/html/2602.22817v1#S4.SS2.p3

Note Builds shell-action advantages from episode returns, structural action residuals and action-history tree margins. Requires Bash parsing and cross-rollout structural comparisons. Source: https://arxiv.org/html/2605.08013v1#S3.SS3.p1

Note Introduces intermediate feedback from tool-execution and answer-presence verifiers for turn-level policy learning. The supervision depends on the task's verifiable intermediate outcomes. Source: https://arxiv.org/html/2505.11821v1#S3.SS2.p2

Note Uses prefix-conditioned gold-answer likelihoods from a frozen initial policy to construct turn-level credit, then combines temporal propagation with outcome anchoring. Evaluated on long-horizon search with compact answers. Source: https://arxiv.org/html/2607.13988v1#S3.SS2.SSS0.Px1.p1

Note Routes trajectory advantage over observed support and error dependencies using positive bounded action weights. Its dependency graph is supplied by a frozen annotator. Source: https://arxiv.org/html/2608.24588v2#Sx4.p1

Note Structured comparison of credit units, feedback, comparison mechanisms and additional supervision. JSON: https://hoyant-su-agentic-rl.hf.space/topics/comparisons/credit-assignment-methods.json