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arxiv: 2605.25511 · v1 · pith:HLMWWSUVnew · submitted 2026-05-25 · 💻 cs.CL

CRPO: Character-centric Group Relative Policy Optimization for Role-aware Reasoning in Role-playing Agents

Yihong Tang , Kehai Chen , Liang Yue , Benyou Wang , Min Zhang This is my paper

Pith reviewed 2026-06-29 21:48 UTC · model grok-4.3

classification 💻 cs.CL
keywords role-playing agentsreinforcement learningGRPOcharacter fidelitystyle collapsepolicy optimizationpersona alignmentLLM reasoning
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The pith

CRPO realigns RL for role-playing agents by decoupling task logic from stylistic rewards to preserve character fidelity.

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

The paper claims that applying standard Group Relative Policy Optimization to role-playing agents causes loss of character fidelity because it favors context-specific utility over persona alignment. CRPO counters this by separating the optimization of task logic from stylistic rewards, adapting constraints dynamically to each character's complexity, and treating generic responses as negative baselines. These changes are presented as resolving gradient conflicts and stopping reversion to a common distribution. A reader would care if the result is agents that stay consistent in emotion and style while still gaining from RL-based reasoning improvements. The work positions this as a direct fix for a mismatch between problem-centric RL and persona-driven tasks.

Core claim

CRPO is a framework that realigns RL objectives with the role-playing task. It improves character distinctiveness through three mechanisms: decoupling task logic from stylistic rewards to resolve gradient conflicts, dynamically adapting optimization constraints based on character complexity, and utilizing generic responses as negative baselines to prevent the model from reverting to a common distribution. Extensive experiments demonstrate that CRPO outperforms existing methods in consistency, emotion and others.

What carries the argument

Character-centric Group Relative Policy Optimization (CRPO), which decouples task logic from stylistic rewards, adapts constraints dynamically, and uses generic responses as negative baselines.

If this is right

  • Role-playing agents maintain higher character distinctiveness and emotional consistency while retaining RL reasoning gains.
  • Gradient conflicts between utility and style objectives are resolved by the separation of reward signals.
  • Optimization constraints adjust automatically to character complexity, reducing the need for manual tuning.
  • Generic responses serve as effective negative baselines that block collapse to a common output distribution.
  • The approach yields measurable gains over prior methods on consistency and emotion metrics.

Where Pith is reading between the lines

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

  • The same decoupling pattern could extend to other multi-objective RL settings where utility and style or persona compete.
  • Agents trained this way might support longer interactive sessions without gradual loss of persona traits.
  • The negative baseline technique may generalize to preventing mode collapse in other persona-conditioned generation tasks.

Load-bearing premise

The decoupling of task logic from stylistic rewards, along with dynamic constraint adaptation, can be implemented scalably across characters without new instabilities or per-character hyperparameter search.

What would settle it

Running the paper's role-playing benchmarks with CRPO and finding no measurable gain in character consistency or emotion metrics relative to standard GRPO would falsify the central improvement claim.

Figures

Figures reproduced from arXiv: 2605.25511 by Benyou Wang, Kehai Chen, Liang Yue, Min Zhang, Yihong Tang.

Figure 1
Figure 1. Figure 1: Problem-centric optimization suffers from ambiguous rewards, rigid constraints, and style collapse, which impede consistent persona alignment. transformation from behaviorist imitation to cognitivist reasoning. Traditional Supervised Fine-Tuning (SFT), representative of the former, often focuses on imitating the shallow linguistic style of characters. Conversely, recent preference-optimization methods have… view at source ↗
Figure 2
Figure 2. Figure 2: The overall framework of CRPO. The method synergizes three core mechanisms to enhance role-playing: (1) Contrastive Anchor Sampling prevents style collapse by introducing generic negative samples; (2) Dual-Stream Advantage Estimation decouples task and style rewards to resolve optimization conflicts; and (3) Entropy-Aware Adaptive Exploitation dynamically regulates gradient updates based on character-speci… view at source ↗
Figure 3
Figure 3. Figure 3: Analysis of training dynamics. 4.3. Ablation Studies We investigate the contribution of each component in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Human evaluation results. 4.4. Human Evaluation To validate real-world utility, we conducted a blinded, pairwise human evaluation involving 4 experts across 100 interaction sessions. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Impact of yanchor quantity on the CharacterBench. Dynamics of Character Identification Entropy Figure 3c illustrates the evolutionary dynamics of training stability. CRPO exhibits a distinct high-exploration, sharp-convergence phase transition. The initially high Hid validates our cautious exploration strategy driven by instance-level weighting, preventing early mode collapse. The subsequent precipitous dr… view at source ↗
Figure 6
Figure 6. Figure 6: The t-SNE visualization of hidden states of the model under different role group. Visualization of Character Representations We visual￾ize the latent representations of responses across different role groups using t-SNE ( [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The role-playing prompt for training [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: All reward curves for Qwen3-8B. 0 50 100 0.00 0.05 0.10 0.15 BLEU-1 0 50 100 0.00 0.02 0.04 0.06 BLEU-2 0 50 100 0.00 0.01 0.02 0.03 BLEU-3 0 50 100 0.000 0.005 0.010 0.015 0.020 BLEU-4 0 50 100 0.0 0.1 0.2 0.3 0.4 Focus 0 50 100 0.00 0.05 0.10 0.15 Focus Attr. 0 50 100 0.00 0.25 0.50 0.75 1.00 Format 0 50 100 0.0 0.1 0.2 ROUGE-1 0 50 100 0.00 0.02 0.04 ROUGE-2 0 50 100 0.00 0.05 0.10 0.15 ROUGE-L 0 50 100… view at source ↗
Figure 9
Figure 9. Figure 9 [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
read the original abstract

Recent advancements in Reinforcement Learning (RL), particularly Group Relative Policy Optimization (GRPO), have significantly enhanced the reasoning capabilities of Large Language Models. However, applying these problem-centric optimization methods to role-playing agents often leads to a loss of character fidelity and style collapse, as they prioritize context-specific utility over persona alignment. To address this, we propose Character-Centric Group Relative Policy Optimization (CRPO), a framework designed to realign RL objectives with the role-playing task. CRPO improves character distinctiveness through three mechanisms: decoupling task logic from stylistic rewards to resolve gradient conflicts, dynamically adapting optimization constraints based on character complexity, and utilizing generic responses as negative baselines to prevent the model from reverting to a common distribution. Extensive experiments demonstrate that CRPO outperforms existing methods in consistency, emotion and others.

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

1 major / 0 minor

Summary. The paper introduces Character-Centric Group Relative Policy Optimization (CRPO) to adapt Group Relative Policy Optimization (GRPO) for role-playing agents. It claims that CRPO addresses the loss of character fidelity and style collapse in standard RL methods by decoupling task logic from stylistic rewards, dynamically adapting optimization constraints based on character complexity, and using generic responses as negative baselines. The authors assert that extensive experiments show CRPO outperforms existing methods in terms of consistency, emotion, and other metrics.

Significance. If the results hold, this work would offer a targeted improvement to RL-based fine-tuning for role-playing LLMs, helping to balance reasoning capabilities with persona consistency, which is a key challenge in deploying such agents.

major comments (1)
  1. [Abstract] Abstract: The abstract states that 'extensive experiments demonstrate that CRPO outperforms existing methods in consistency, emotion and others' but provides no quantitative results, baselines, metrics, statistical tests, or implementation details. This absence makes it impossible to assess or verify the central empirical claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for highlighting the need for greater specificity in the abstract. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract states that 'extensive experiments demonstrate that CRPO outperforms existing methods in consistency, emotion and others' but provides no quantitative results, baselines, metrics, statistical tests, or implementation details. This absence makes it impossible to assess or verify the central empirical claim.

    Authors: We agree that the current abstract is too high-level and does not provide enough quantitative grounding for the central claim. In the revised version we will expand the final sentence of the abstract to report the primary evaluation metrics (character consistency, emotion alignment, and style distinctiveness), the key baselines (standard GRPO and SFT), and the magnitude of the observed improvements (e.g., relative gains and statistical significance where computed). This change will make the empirical contribution verifiable directly from the abstract while preserving its brevity. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and description present CRPO as a new framework with three high-level mechanisms (decoupling rewards, dynamic constraints, generic baselines) without any equations, fitted parameters, or derivation steps that reduce to self-defined quantities or prior self-citations. No load-bearing claims rely on the authors' own previous work in a way that creates definitional equivalence or forced predictions. The central claim remains independent of its inputs based on the available text.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the approach implicitly relies on standard RL policy optimization assumptions without detailing any new fitted quantities or postulates.

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discussion (0)

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Reference graph

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