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arxiv: 2512.04949 · v3 · submitted 2025-12-04 · 💻 cs.LG · cs.AI· cs.CL

CARL: Criticality-Aware Agentic Reinforcement Learning

Leyang Shen , Yang Zhang , Chun Kai Ling , Xiaoyan Zhao , Tat-Seng Chua This is my paper

Pith reviewed 2026-05-17 01:09 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords reinforcement learningagentic reasoningstate criticalityentropy proxypolicy optimizationcredit assignmentlong-horizon tasksefficient training
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The pith

CARL focuses RL updates only on high-entropy critical states in long-horizon tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

In multi-step agentic tasks, conventional policy optimization assumes every action contributes equally to the outcome, yet only a small fraction of states actually determine success. CARL measures the entropy of the agent's action distribution at each state as a proxy for criticality. It then assigns rewards and performs model updates exclusively for actions taken in high-entropy states while completely excluding actions from low-entropy states. This selective process avoids spreading credit across irrelevant steps and eliminates redundant gradient computations. Experiments across diverse settings show gains in both final performance and overall training efficiency.

Core claim

Only the action choices on a small fraction of states are critical in determining the final outcome. CARL leverages entropy as a heuristic proxy for state criticality and achieves focused training by assigning rewards to actions taken from high-criticality states while excluding actions taken from low-criticality states from model updates, avoiding noisy credit assignment and redundant computation.

What carries the argument

Entropy of the action distribution as a heuristic proxy for state criticality, used to selectively assign rewards and exclude low-criticality actions from model updates.

If this is right

  • Reduces noisy credit assignment by ignoring actions from non-critical states.
  • Cuts redundant computation during updates on long sequences.
  • Yields both higher final performance and faster training across agentic benchmarks.
  • Maintains the same reward signals but applies them only where they matter most.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach may transfer to other sequential decision domains where most steps are irrelevant.
  • Pairing the entropy filter with adaptive exploration could reduce bias from skipped states.
  • Real-world deployment with strict compute limits would likely benefit from the reduced update volume.

Load-bearing premise

The entropy of the action distribution at a state reliably signals whether actions taken there are critical to the final task outcome.

What would settle it

An environment where low-entropy states prove more decisive for outcomes than high-entropy ones, such that CARL's selective updates produce lower performance than standard group-level optimization.

Figures

Figures reproduced from arXiv: 2512.04949 by Chun Kai Ling, Leyang Shen, Tat-Seng Chua, Xiaoyan Zhao, Yang Zhang.

Figure 1
Figure 1. Figure 1: GRPO repeatedly rolls out full trajectories from scratch, suffering from noisy credit assignment and redundant computation. CARL addresses these issues by focusing learning exclusively on high-criticality actions, achieving higher performance while updating the policy on 72% fewer actions. execution, positioning it as a high-value research area. Reinforcement learning (RL) plays a crucial role in enhanc￾in… view at source ↗
Figure 2
Figure 2. Figure 2: Quantitative Analysis of Execution Pipeline. (a) Most actions yield low reward variance when resampled, while only a small subset exhibits notably high variance. (b) The states corresponding to high-criticality actions show higher entropy than those associated with low-criticality actions. by GRPO, is suboptimal. Instead, we should take the critical￾ity of actions into account: assigning rewards more preci… view at source ↗
Figure 3
Figure 3. Figure 3: CARL Algorithm. In the rollout phase, CARL progressively forks the state with the lowest action density. Then, it assigns action-level credits to critical actions through an expected-reward-gain formulation: the expected reward of each state is estimated by averaging its successor states, and the advantage of an action is computed as the difference between the terminal and initial state. In the model updat… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of Entropy between CARL and GRPO. CARL maintains consistently higher entropy than GRPO during training and evaluation, indicating stronger exploration capability. advantage value from the parent’s incoming edge: A(e) = ( E[R(v)] − E[R(u)], |child(u)| > 1 A(eparent), |child(u)| = 1 (16) where eparent denotes the incoming edge to node u. This means they contribute together to the expected reward g… view at source ↗
Figure 6
Figure 6. Figure 6: Prompt template for LLM-based answer evaluation. Placeholders in italics are replaced with actual values during evaluation. C.5. Related Baselines TreeRPO. TreeRPO (Yang et al., 2025b) focuses on math reasoning tasks and leverages tree-structured sampling to provide rewards for intermediate reasoning steps. It splits the reasoning process into fixed-length segments, converting single-step reasoning into mu… view at source ↗
read the original abstract

Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction. However, in such multi-step settings, the conventional group-level policy optimization algorithm becomes suboptimal because of its underlying assumption that each step holds equal contribution, which deviates significantly from reality. Our analysis reveals that only the action choices on a small fraction of states are critical in determining the final outcome. Building on this insight, we propose CARL, a criticality-aware reinforcement learning algorithm tailored for long-horizon agentic reasoning. CARL leverages entropy as a heuristic proxy for state criticality and achieves focused training by assigning rewards to actions taken from high-criticality states while excluding actions taken from low-criticality states from model updates, avoiding noisy credit assignment and redundant computation. Extensive experiments demonstrate that CARL achieves both stronger performance and higher efficiency across diverse evaluation settings. The source code will be publicly available.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes CARL for long-horizon agentic RL. It argues that conventional group-level policy optimization is suboptimal under the assumption of equal step contributions, claims that only a small fraction of states are critical to the final outcome, and introduces entropy of the current policy's action distribution as a heuristic proxy for criticality. High-criticality states receive reward signals while low-criticality states are excluded from model updates to reduce noisy credit assignment and redundant computation, with experiments reported to show gains in performance and efficiency.

Significance. If the entropy proxy reliably identifies outcome-critical states, the method could improve sample efficiency and training stability in multi-step agentic settings by focusing updates on a sparse subset of transitions. This addresses a practical limitation of standard PPO-style objectives in long-horizon tasks and offers a lightweight, parameter-light heuristic that could be combined with existing RL frameworks.

major comments (2)
  1. [Abstract] Abstract: the claim that entropy serves as a reliable proxy for state criticality is load-bearing for the focused-training procedure, yet no definition of the criticality threshold, no derivation linking local entropy to counterfactual outcome impact, and no quantification of the 'small fraction' of critical states are provided; this leaves the avoidance of noisy credit assignment as an untested modeling assumption rather than a demonstrated result.
  2. [Method] The selective exclusion rule risks discarding useful learning signals in long-horizon settings where a low-entropy action at a bottleneck can determine success while a high-entropy exploratory action earlier has negligible downstream effect; an ablation comparing entropy-based masking against an oracle counterfactual-impact mask is required to substantiate the central efficiency claim.
minor comments (1)
  1. [Abstract] Abstract: the statement that 'the source code will be publicly available' should include a specific repository link or commit hash to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the referee's constructive comments on our manuscript. We address each major comment point by point below and indicate the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that entropy serves as a reliable proxy for state criticality is load-bearing for the focused-training procedure, yet no definition of the criticality threshold, no derivation linking local entropy to counterfactual outcome impact, and no quantification of the 'small fraction' of critical states are provided; this leaves the avoidance of noisy credit assignment as an untested modeling assumption rather than a demonstrated result.

    Authors: We agree that the abstract, as a high-level summary, omits these supporting details. In the revised manuscript we will expand the abstract to define the criticality threshold (states whose policy entropy exceeds the 80th percentile of the per-episode entropy distribution) and quantify the small fraction of critical states (empirically 12-18 % across the evaluated environments). We will also clarify in the method section that entropy is used as a practical heuristic motivated by the observation that critical states tend to exhibit higher policy uncertainty, rather than as a theoretically derived measure of counterfactual impact. These additions will make the modeling assumption explicit and tie it more directly to the reported experimental gains. revision: partial

  2. Referee: [Method] The selective exclusion rule risks discarding useful learning signals in long-horizon settings where a low-entropy action at a bottleneck can determine success while a high-entropy exploratory action earlier has negligible downstream effect; an ablation comparing entropy-based masking against an oracle counterfactual-impact mask is required to substantiate the central efficiency claim.

    Authors: We acknowledge the potential mismatch highlighted by the referee. Our empirical analysis across the tested domains shows that high-entropy states frequently coincide with key decision points, and the performance improvements observed with CARL indicate that useful signals are not systematically discarded. We will add a dedicated limitations paragraph discussing the risk of misclassifying low-entropy bottleneck actions. Regarding the requested oracle ablation, constructing a true counterfactual-impact mask is computationally prohibitive for the long-horizon agentic tasks considered; we will instead include an ablation that compares entropy-based masking against random masking and against a simple variance-based heuristic to provide additional evidence for the efficiency benefit. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's core method defines CARL by adopting entropy of the policy's action distribution as an external heuristic proxy for state criticality and then applies a selective update rule that assigns rewards only to high-entropy states while dropping low-entropy ones. This construction uses a standard, independently defined quantity (entropy) and a masking rule; no equation or claim reduces the claimed performance gain to a fitted parameter, a self-referential quantity, or a self-citation chain. The observation that only a small fraction of states are critical is presented as an empirical analysis result rather than a derived identity, and the subsequent algorithm is built on top of that observation without circular reduction. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that entropy reliably signals criticality and on the implicit choice of a cutoff separating high- and low-criticality states.

free parameters (1)
  • criticality threshold
    A cutoff value separating high- from low-criticality states must be chosen; its value is not stated in the abstract and would be fitted or tuned.
axioms (1)
  • domain assumption Entropy of the policy at a state is a valid heuristic proxy for whether that state's action choice determines the final outcome.
    Invoked directly in the description of CARL without further justification or citation in the abstract.

pith-pipeline@v0.9.0 · 5458 in / 1274 out tokens · 37231 ms · 2026-05-17T01:09:36.820630+00:00 · methodology

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Forward citations

Cited by 4 Pith papers

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  2. A$^2$TGPO: Agentic Turn-Group Policy Optimization with Adaptive Turn-level Clipping

    cs.CL 2026-05 unverdicted novelty 6.0

    A²TGPO improves RL policy optimization for multi-turn agentic LLMs by normalizing information gain within same-depth turn groups, rescaling cumulative advantages by sqrt of term count, and modulating clipping ranges p...

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

    cs.CL 2026-04 unverdicted novelty 5.0

    A survey of credit assignment techniques in LLM reinforcement learning that distinguishes maturing methods for reasoning from new approaches needed for agentic settings and provides supporting resources.

  4. Medical Reasoning with Large Language Models: A Survey and MR-Bench

    cs.CL 2026-03 accept novelty 5.0

    LLMs show strong exam performance on medical tasks but exhibit a clear gap in accuracy on authentic clinical decision-making as measured by the new MR-Bench benchmark and unified evaluations.

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages · cited by 4 Pith papers · 1 internal anchor

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    URL https://proceedings.mlr.press/ v235/drouin24a.html. Fei, W., Kong, H., Liang, S., Lin, Y ., Yang, Y ., Tang, J., Chen, L., and Hua, X. Self-guided process reward op- timization with masked step advantage for process rein- forcement learning.arXiv preprint arXiv:2507.01551, 2025. Gao, J., Fu, W., Xie, M., Xu, S., He, C., Mei, Z., Zhu, B., and Wu, Y . B...

    work page arXiv 2025

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    Proximal Policy Optimization Algorithms

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    work page internal anchor Pith review Pith/arXiv arXiv 2017