From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

Chenchen Zhang

cs.CLApr 10, 2026

From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

Authors: Chenchen Zhang

Organizations: Independent Researcher

Abstract

Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse outcome rewards, yet such rewards say little about which token, reasoning step, tool call, memory operation, or agent caused an outcome. This credit assignment (CA) problem spans reasoning RL and becomes sharper in agentic RL, where environment interaction introduces transition non-closure, partial observability, limited replay, heterogeneous actions, weak intermediate verifiability, and agent coupling. We synthesize a unified corpus of 69 papers published from January 2024 through July 31, 2026: 56 core CA methods and 13 adjacent or boundary enablers, selected from 92 deduplicated screening records. We retain the original granularity-by-methodology taxonomy and add a six-diagnostic framework mapping assumption breaks to identification barriers, estimators, and evaluation controls. A source-located full-text audit covers a fixed 42-core-paper subset. Two algorithm researchers independently and blindly cross-coded 252 diagnostic cells, agreeing on 223 (88.5%); per-diagnostic Cohen's kappa ranges from .543 to .909, and principal-family agreement is 42/42 (kappa=1.000). Beyond taxonomy, we establish when restored-state comparisons identify a protocol-specific causal contrast, show that text-only histories can leave even the sign of credit unidentified, and introduce a reusable CA-ID Card linking each claim to its estimand, evidence provenance, and falsification test. An atomic reporting audit describes comparator, budget parity, ablation, overhead, uncertainty, and replay coverage without constructing a cross-paper leaderboard. The companion repository hosts a living catalog and decision aids; a dated release of the frozen audit bundle is planned there separately from the minimal arXiv source.

Figures & tables

Panel of Figure 1 — Figure 1: Evolution of RL for LLMs and the corresponding credit assignment challenges. Each phase introduces longer trajectories, more complex environments, and harder credit assignment problems. The shift from reasoning to agentic RL represents a qualitative leap in CA difficulty.

Symbol	Description
$x$	Input prompt / task description
$y=(y_{1},\ldots,y_{L})$	Generated token sequence (response)
$\tau$	Complete trajectory (episode)
$s_{t}$	State at time step $t$
$a_{t}$	Action at time step $t$ (token or turn-level response)
$o_{t}$	Observation at time $t$ (in POMDP settings)

Table 1: Summary of key notation used throughout this paper.

Flag	Diagnostic break	Identification barrier	Minimum evaluation control	Count
T	Transition non-closure	Same text need not imply same effective dynamics	Restore environment and hidden execution state	24/42
O	Partial observability	Logged history may omit outcome-relevant state	State sufficiency argument or privileged-state audit	10/42
R	Limited replay	Intervention cannot be reproduced exactly	Declare replay provenance and replica noise floor	19/42
H	Heterogeneous actions	Token, tool, memory, and message units are not exchangeable	Typed-unit and unit-mismatch ablations	20/42
V	Weak local verifiability	Intermediate correctness is unavailable or proxy-based	Calibrate judge/critic against intervention or continuation	32/42
C	Agent coupling	One agent’s action changes another agent’s policy path	Message, role, or coalition interventions	7/42

Table 2: Six diagnostic breaks, their identification barriers, and minimum controls. Counts refer only to the fixed 42-core-paper full-text subset.

Field	Required declaration	Evidence or falsification control
Unit and estimand	Token, segment, turn, tool, memory, or message; reference $q_{u}$ ; target contrast	Show that conclusions survive plausible unit and reference choices
Effective state	Contents of $z_{u}^{-}$ ; observed, matched, restored, or latent	Checkpoint identity or state-sufficiency argument; label text-only matching
Intervention provenance	Actual rerun, decoder resume, environment restore, logged match, or model/proxy	Compare weaker replay/proxy to exact or checkpointed replay on a shared subset
Downstream protocol	Continuation policy, horizon, reward/verifier, and noise coupling $\rho$	Vary continuation/verifier; report common-random-number or reroll protocol
Support and uncertainty	Action overlap, branch repetitions, interval or power target	Wrong sign, no overlap, or failure to clear preregistered tolerance invalidates the claim
Optimization interface	How credit enters reward, advantage, mask, target, or loss weight	CA-specific ablation under matched model, data, optimizer, and trajectory budget

Table 3: The CA-ID Card: a minimum claim contract, not a guarantee that causal identification is possible.

Panel of Figure 2 — Figure 2: Representative two-dimensional taxonomy of credit assignment methods for LLM RL, organized by the original assignment-granularity (vertical) and computational-methodology (horizontal) axes. Blue : primarily reasoning RL; Red : primarily agentic RL; Purple : multi-agent. The dashed arrow preserves the original reasoning-to-agentic evolution trend. The complete 69-paper taxonomy is provided in Table 11 ; a machine-readable frozen snapshot is planned as a dated companion-repository release.

Setting	Turns	Tokens	“Decision points”
Reasoning RL (GSM8K)	1	200–800	3–10 steps
Reasoning RL (MATH)	1	1,000–5,000	5–20 steps
Reasoning RL (AIME/competition)	1	10,000–30,000+	20–100 steps
Agentic RL (ALFWorld/WebShop)	5–20	5,000–30,000	5–20 turns
Agentic RL (WebArena)	10–30	30,000–100,000	10–30 turns
Agentic RL (SWE-bench)	20–100+	100,000–500,000+	20–100+ turns

Table 4: Trajectory complexity across reasoning and agentic RL settings. Agentic tasks involve dramatically more turns, tokens, and decision points, creating qualitative challenges for credit assignment.

Dimension	Reasoning RL	Agentic RL
Effective-state closure	Often assumed from prefix	Often broken by hidden environment state
Replay provenance	Text branch or decoder resume	Environment checkpoint, logged match, or proxy
Observability	Prefix often treated as sufficient	Partial environment observation common
Typical horizon	1 turn, 0.5K–30K tokens	10–100+ turns, 100K–1M tokens
Action types	Homogeneous (tokens)	Heterogeneous (tool, plan, communicate)
Intermediate verification	Often possible (math)	Rarely possible

Table 5: Typical diagnostic conditions in reasoning and agentic studies; these are tendencies, not defining binaries.

Method	Stochastic	Partial	Long	Heterog.	Non-verif.	Bifurcation
	Env.	Obs.	Horizon	Actions	States	Points
ArCHer	$\circ$		✓
AgentPRM	✓		$\circ$
SWEET-RL	$\circ$	✓	$\circ$		✓
HCAPO	✓	$\circ$	$\circ$		✓	$\circ$
C3 / CCPO	$\circ$		$\circ$		✓	✓

Table 6: Mapping agentic challenges ( Section 4 ) to methods that address them. ✓= directly addresses;

\circ

= partially addresses. † HICRA is a reasoning RL method whose planning/procedural distinction provides transferable insights for these agentic challenges.

Method	Granularity	Methodology	Setting	Type	Aux. Model?	Compute	Venue	Year
Reasoning RL Methods
VinePPO	Token	MC	R	C	No	High	ICML’25	2025
RED	Token	Redistribution	R	C	RM	Low	—	2024
T-REG	Token	Self-generated	R	C	No	Low	—	2024
From r to Q*	Token	Implicit	R	C	No	—	—	2024
SPO	Segment	MC	R	C	No	Med	—	2025

Table 7: Legacy structured comparison for the original long-form rows 1–47; it is not a comprehensive table for the 69-paper corpus. Setting : R = Reasoning, A = Agentic, M = Multi-Agent. Type : C = core CA method; E = adjacent/boundary enabler. Compute labels are qualitative author coding, not matched measurements.

Atomic field	Audited counts
Matched episode/trajectory comparator	40 yes; 2 no
Same model/data/budget parity	19 yes; 21 partial; 2 not applicable
CA-specific ablation	39 yes; 3 no
CA-specific overhead	21 numeric; 2 numeric proxy; 12 qualitative; 6 NR; 1 not applicable
Uncertainty evidence	10 repeated training; 7 repeated evaluation only; 1 evaluation bootstrap only; 1 significance test only; 2 unclear; 21 NR

Table 8: Source-located reporting coverage in the fixed 42-core-paper subset. Categories are mutually exclusive within each row.

Scenario	Characteristics	Promising Candidates	Key Consideration
Math reasoning (GSM8K, MATH)	Short CoT, often prefix-closed, verifier available	GRPO (baseline), PURE, SPO, SPRO	Declare verifier and branch protocol
Hard math/competition (AIME, IMO)	Long CoT (10K–30K), verifiable	VinePPO, HICRA, CAPO	Compute budget scales with CoT length
Tool-use agents (WebShop, ALFWorld)	5–20 turns, partially verifiable tools	GiGPO, AgentPRM, Turn-PPO	Critic-free preferred for efficiency
Web navigation (WebArena)	10–30 turns, stochastic, POMDP	SWEET-RL, HCAPO, IGPO	Privileged critic exploits training info
Software engineering (SWE-bench)	50–100+ turns, very long context, non-verifiable	CARL, HCAPO, C3/CCPO, ArCHer	Sparse credit + hindsight analysis
Multi-agent systems	Cross-agent credit, communication	M-GRPO, C3, LLM-MCA	Decomposition across agents is key

Table 9: Practical guidance for selecting credit assignment methods based on task characteristics. Methods listed as promising candidates based on evaluation settings and design properties; actual performance depends on base model, data, and infrastructure. “Directly evaluated” methods have been tested on the listed scenario; others are listed based on design suitability.

Panel of Figure 4 — Figure 4: Structure-preserving update of the method-selection decision tree for credit assignment in LLM RL. Leaves match methods to the original task-setting, horizon, compute, and auxiliary-model predicates; they are design-property candidates, not a cross-paper performance ranking. The token and turn thresholds are coarse routing heuristics inherited from the original decision aid, not validated performance cutoffs. Suitability remains conditional on the declared CA-ID estimand, replay provenance, verifier validity, base model, data, and budget.

Panel of Figure 5 — Figure 5: Temporal distribution of all 69 included papers through July 31, 2026. Stacks use Reasoning, Agentic-or-mixed, and Multi-Agent settings; A/M papers are counted as Agentic-or-mixed. Canonical arXiv identifiers determine the half-year bins when available. The starred 2026 H1 bar includes GVPO, whose frozen OpenReview record specifies only the year 2026, as a disclosed display convention rather than an inferred posting date. The dashed 2026 H2-to-date † bar covers July only and is therefore a partial-period count, not an equal-duration comparison with the preceding half-year bins. Bar totals are shown above each stack.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Abbreviation	Full Name	Reference	Section
ACPO	Attribution-based Credit for RLVR	Yin et al. (2025)	§ 3.3
AgentPRM	Process Reward Model for LLM Agents	Xi et al. (2025)	§ 5.1
ArCHer	Actor-Critic with Hierarchical Evaluation	Zhou et al. (2024c)	§ 5.4
C3	Contextual Counterfactual Credit	Chen et al. (2026b)	§ 5.2
CAPO	Credit Assignment Policy Optimization	Xie et al. (2025)	§ 3.3
CARL	Critical Action Reinforcement Learning	Shen et al. (2025)	§ 5.4

Appendix

Table 10: Alphabetical index of credit assignment methods reviewed in this paper.

#	Method	Type	Setting	Gran.	Methodology	BL	Ev.	Primary Benchmarks
Reasoning RL — Core CA Methods (15)
1	VinePPO	C	R	Token	MC	P	S	GSM8K, MATH
2	RED	C	R	Token	Redistribution	P	L	MATH
3	T-REG	C	R	Token	Self-generated	P	L	GSM8K, MATH
4	From $r$ to $Q^{*}$	C	R	Token	Implicit	D	A	Theoretical analysis
5	SPO	C	R	Segment	MC	G	S	MATH-500, GSM8K

Appendix

Table 11: Unified inventory, Part I: original long-form rows 1–47 (39 core and 8 adjacent/boundary after consistent reclassification).

#	Method	Type	Setting	Gran.	Methodology	BL	Ev.	Primary Benchmarks
May-to-July additions integrated into the unified snapshot (22)
48	GVPO	C	A	Step/Turn	Verifier shaping	G	S	AppWorld agents
49	A 3	C	A	Turn/Action	Structured Adv.	G	S	ShellOps CLI
50	A 2 TGPO	C	A	Turn	IG/Clipping	G	L	Tool agents
51	AutoResearch	E	A	Trial	Lineage feedback	—	NC	Research agents
52	Memory-R2	C	A	Memory op.	Matched-state groups	—	NC	Memory agents

Appendix

Table 12: Unified inventory, Part II: rows 48–50 complete the fixed 42-core-paper audit; rows 51–69 are frontier additions outside that full-text audit. Parts I–II total 56 core and 13 adjacent/boundary papers.

Category	Priority	Item
Model & Data	Required	Base model name, size, and version
Model & Data	Required	Training data: source, size, and any filtering applied
CA Method	Required	Credit granularity (token / segment / step / turn / multi-agent)
CA Method	Required	Methodology family per our taxonomy
Baselines	Required	At least one episode-level baseline (GRPO or PPO) with identical base model
	Required	Baseline training recipe: same compute budget or explicit compute comparison

Appendix

Table 13: Recommended reporting checklist for credit assignment papers in LLM RL.

Diagnostic	Agreement	Percent	Cohen’s $\kappa$	Disagreements
Transition non-closure	39/42	92.9%	.856	3
Partial observability	33/42	78.6%	.557	9
Limited replay	37/42	88.1%	.762	5
Action heterogeneity	37/42	88.1%	.759	5
Weak local verifiability	36/42	85.7%	.543	6
Agent coupling	41/42	97.6%	.909	1

Appendix

Table 14: Independent blind cross-coding reliability in the fixed 42-paper subset.

Credit Assignment in RL

Credit assignment in reinforcement learning studies how to trace rewards and failures back to the actions, states, or agents that caused them.

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Jul 8, 2026

Right Answer, Wrong Tokens: Tail-Aware Credit Calibration for RLVR

Although reinforcement learning (RL) improves the reasoning capabilities of large language models (LLMs), GRPO-style methods can suffer from a positive-credit contamination failure mode. Specifically, uniform credit assignment can reinforce locally erroneous tail tokens alongside useful tokens within successful trajectories. To address this problem, we propose Tail-Aware Credit calibratiOn (TACO), which calibrates the positive credit assigned to such tokens without token-level error labels. To this end, TACO lets the policy serve as its own reference, assessing each sampled token's rarity relative to the policy's local expectations to compute a tail-risk score. Using this score as a proxy for potential local errors, TACO softly reduces positive credit for high tail-risk tokens while retaining nonzero weights, limiting their reinforcement without eliminating opportunities to learn useful rare patterns. Experiments across four LLMs and eight reasoning benchmarks show that TACO consistently improves over GRPO-style baselines. Notably, TACO also improves training stability, supporting sustained performance gains in long-horizon RL. The source code is available at: https://github.com/xiuyilou/TACO.

Xiuyi Lou, Zicheng Xu, Yu-Neng Chuang +4

Johns Hopkins University · Rice University · Workato +1

RL for Language Models Token-Level Credit Assignment Credit Assignment in RL Critic-Free RL RL Fine-Tuning

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Aug 20, 2026

Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay

Audited against policy-conditional ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signals we audit -- LLM-judge scores, outcome-conditioned logprob ratios, or the policy's own confidence -- shows reliable incremental fidelity beyond its own marginal-matched shuffled control. Correcting for replay-target reliability leaves implicit fidelity bounded near zero and judge fidelity inconclusive at the achieved target reliability. Existing evaluations grade these signals against annotated step correctness; we audit them against step contribution -- what re-sampling the policy's own alternatives at each decision point and rolling forward changes about the outcome -- and they come apart. The ground truth is structured: 30.5% of decision points where it is defined exhibit a nonzero replay contrast at the achieved sampling resolution, and measurability is model-dependent -- the fraction of points with no policy-supported counterfactual differs twofold (13.1% vs. 26.8%) between two similar-scale policies. The failure mode is identifiable: implicit credit echoes the policy's fluency (median rank correlation +0.75, replicating at +0.70 in a second family under a corrected instrument), while outcome conditioning adds no causal information (partial correlation -0.004, Qwen). A confidence-only router recovers pivotal steps at chance level, but cuts judge cost by 13.1% per turn (14.0% per trajectory). In a seven-arm pre-registered training experiment, no arm reliably outperforms the untrained policy, and the checkpoints' apparent instrument signature is statistically consistent with mediation by effective training dose in this design -- sparser credit retains fewer examples, an order-of-magnitude spread in optimizer steps -- not credit content. Comparisons of credit rules must match effective sample size, or they measure dose, not credit.

Haiyue Zhang

University of Southern California Los Angeles, CA, USA

LLM Agent Evaluation Step-Level Credit Assignment in RL Counterfactual Credit Assignment LLM-as-a-Judge

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Oct 1, 2026

My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learning

Agentic reinforcement learning (RL) has emerged as a powerful approach for training large language model agents on multi-step tasks, yet reliance on terminal outcome rewards creates two credit-assignment problems, particularly in long-horizon tasks. First, same-outcome rollout groups provide no learning signal from terminal rewards. Second, terminal rewards provide only trajectory-wide feedback, making it difficult to identify which decisions caused a failure. Recent work supplements terminal rewards with finer-grained information from trajectory analysis, such as natural-language reflections on intermediate decisions and errors. However, natural-language diagnoses are difficult to use directly for credit assignment: their error claims may be unreliable, and they do not quantify how much each error should affect learning. We propose Self-Diagnosis-guided Terminal Credit Redistribution (FAULT), which turns diagnosed errors into explicit step-level credit anchored by terminal outcomes. FAULT checks diagnostic evidence and learns relative error costs from task outcomes. During training, the policy and self-diagnoser co-evolve, while error costs are updated online from recent outcomes. On ALFWorld, FAULT recovers learning signals from same-outcome groups, reaching 95% signal coverage versus 41% for GRPO and 72% for GiGPO, while better localizing credit to specific error steps. Across two model scales, FAULT delivers strong. improvements on the long-horizon ALFWorld and WebShop tasks while remaining competitive on short-horizon Search-based QA.

Yihua Zhu, Qianying Liu, Weixu Qiao +10

Alibaba Group · Kyoto University · NII LLMC +6

Agentic RL Credit Assignment in RL Step-Level Credit Assignment in RL Credit Assignment Fine-Grained Credit Assignment RL for LLM Agents

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