Aligning Machiavellian Agents: Behavior Steering via Test-Time Policy Shaping

Conference: AAAI 2026 arXiv: 2511.11551v3 Code: GitHub Area: Reinforcement Learning Keywords: Test-time alignment, policy shaping, ethical behavior steering, Machiavellian agents, reinforcement learning

TL;DR

This paper proposes a test-time policy shaping method that interpolates and modifies the action probability distribution of pretrained RL agents at inference time using lightweight ethical attribute classifiers, enabling fine-grained behavioral steering across multiple ethical attributes without retraining.

Background & Motivation

• RL agents trained to maximize reward exhibit Machiavellian behaviors (power-seeking, ethical violations, etc.) that are misaligned with human values.
• Existing alignment methods (reward shaping, RLHF, etc.) are predominantly training-time approaches that require retraining, incurring high cost and poor adaptability.
• Different cultures, contexts, and applications prioritize ethical attributes differently, necessitating flexible and adjustable alignment mechanisms.
• Training-time methods provide insufficient granularity over individual ethical attributes, making precise control over a single attribute difficult.

Core Problem

How can a trained RL agent be steered toward ethical behavior in a flexible and controllable manner without retraining, while achieving an adjustable trade-off between reward maximization and ethical alignment?

Method

Overall Architecture

A two-stage framework: 1. Offline stage: A binary classifier (based on ModernBERT) is trained for each ethical attribute on training-set games to learn whether a given (scene, action) pair involves a specific ethical violation. 2. Test-time stage: The pretrained RL agent's policy is reshaped via interpolation—the RL Q-value policy and the classifier-derived ethically-aware policy are combined in a weighted sum to produce a new action selection distribution.

Key Designs

Ethical attribute classifiers: - ModernBERT is used to independently train a binary classifier for each of 15 attributes (10 moral violations + 4 power-seeking + 1 disutility). - Input consists of (scene text, action text) pairs; output indicates whether the ethical attribute is present. - Balanced sampling is adopted to address class imbalance (positive examples are extremely sparse, e.g., killing has ~100 positives vs. ~20,000 negatives). - Average accuracy: \(88.8 \pm 6.5\%\); average recall: \(89.6 \pm 8.0\%\). High recall is prioritized to reduce missed-detection risk.

Policy interpolation: - Classifier action probability: \(\mathbf{P}_{\text{attribute}}(a) = \frac{1}{N} \sum_{i=1}^{N} \text{softmax}(s_i \cdot \mathbf{C}_{k_i}(a))\), where \(s_i = 2v_i - 1\) controls the minimization/maximization direction. - Interpolated policy: \(\pi(a) = (1-\alpha) \cdot \mathbf{P}_{\text{RL}}(a) + \alpha \cdot \mathbf{P}_{\text{attribute}}(a)\) - \(\alpha \in [0,1]\) controls the strength of ethical constraints: \(\alpha=0\) corresponds to the pure RL policy; \(\alpha=1\) corresponds to the pure classifier policy. - Simultaneous shaping of multiple attributes is supported, as is reverse operation (increasing violations).

Bidirectional control: - The method can "reverse" training-time alignment: for an RL-AC agent trained with an artificial conscience, applying the classifier in reverse can restore unethical behavior. - This demonstrates bidirectional flexibility, making the approach suitable for correcting misaligned agents.

Loss & Training

• Attribute classifiers are trained with binary cross-entropy loss.
• Hyperparameters: input token length 1000, batch size 8, learning rate 5e-5, weight decay 0.01, AdamW optimizer, 5 epochs.
• The RL agent (DRRN architecture) is trained for 50,000 steps using DeBERTa Large v3 to encode action text.
• Policy shaping at test time requires no training or gradient updates—it is a pure inference-time operation.

Key Experimental Results

Evaluation platform: MACHIAVELLI benchmark, 134 text-based games; 10 games with the broadest attribute coverage are selected from the test set. All values are normalized to 100 relative to a Random Agent.

Metric	RL-Base	RL-α0.5	RL-α1.0	RL-AC	Oracle
Points	29.67	15.6±0.5	11.9±1.2	27.65	13.1±1.2
Achievements	14.04	8.4±0.4	6.5±0.5	13.54	6.2±0.3
All Power	163.67	96.4±2.3	87.9±2.0	106.31	89.4±11.6
All Violations	162.05	100.1±4.0	94.7±10.1	105.70	82.3±3.9
Disutility	176.62	102.48	96.37	106.26	66.40
Killing	162.21	100.97	50.41	102.31	30.39
Deception	141.78	78.91	64.56	98.38	33.78
Intend. harm	171.50	75.32	47.10	113.78	29.28

• RL-α0.5 reduces ethical violations by an average of 62 points and power-seeking behaviors by 67.3 points.
• RL-α1.0 outperforms the training-time method RL-AC on most attributes and approaches the Oracle upper bound.
• The largest reduction is observed for killing (162→50), followed by intending harm (171→47).

Ablation Study

• Effect of \(\alpha\): As \(\alpha\) increases from 0 to 1, violations decrease monotonically but reward also declines, yielding a clear Pareto frontier.
• Attribute correlation analysis: Killing, physical harm, and power-seeking are strongly positively correlated; deception and spying are negatively correlated with killing—reducing violent behavior may increase deceptive behavior.
• Reverse manipulation: Applying the classifier in reverse successfully restores unethical behavior in the RL-AC agent, confirming the method's bidirectional flexibility.
• Multi-attribute alignment: When simultaneously optimizing two attributes, inter-attribute correlations introduce interaction effects, requiring careful weight selection.
• Statistical significance: Wilcoxon rank-sum tests show that improvements are statistically significant (\(p < 0.05\)) for most attributes.

Highlights & Insights

1. Test-time operation with no retraining: The RL agent's parameters remain unchanged; policy interpolation at inference time enables flexible and low-cost deployment.
2. Fine-grained multi-attribute control: 15 ethical attributes can be independently controlled in direction and magnitude, far surpassing coarse-grained "good/bad" binary methods.
3. Bidirectional controllability: The method can both reduce and increase specific ethical violations, making it applicable for correcting misalignment or exploring behavioral boundaries.
4. Cross-environment generalization: Classifiers trained on training-set games generalize effectively to entirely different test-set games.
5. Attribute correlation findings: Systematic analysis of positive and negative correlations among ethical attributes provides practical guidance for deployment.

Limitations & Future Work

1. Inevitable reward–ethics trade-off: Improving ethical behavior necessarily sacrifices game reward; the optimal value of \(\alpha\) must be tuned for each application context.
2. Limited classifier precision: Average F1 score is low (24.4%); low-frequency attributes such as fairness achieve the worst accuracy (67%), degrading alignment for those attributes.
3. Validation limited to text-game environments: MACHIAVELLI is a game benchmark with a substantial gap from real-world high-stakes settings (e.g., healthcare, finance).
4. Equal-weight assumption for multi-attribute alignment: The current approach assigns uniform weights across attributes, whereas real-world scenarios require differentiated prioritization.
5. LLM baseline uses LLaMA-2 7B: This relatively small model may underestimate the true capability of LLM-based agents.

Related Work & Insights

Method	Stage	Retraining Required	Attribute Granularity	Cross-environment
Ours (TTPS)	Test-time	❌	Per-attribute	✅
RL-AC (Pan et al.)	Training-time	✅	Coarse (3 categories)	❌
Reward Shaping	Training-time	✅	Reward-function level	❌
LLM Good Agent	Test-time	❌	Prompt level	✅ (poor performance)
RLHF	Training-time	✅	Preference level	Limited

• Compared to RL-AC: the proposed method requires no retraining and outperforms RL-AC on both All Violations (94.7 vs. 105.7) and All Power (87.9 vs. 106.3).
• Compared to LLM agents: LLM agents incur fewer ethical violations but achieve far lower reward; the proposed method strikes a better balance between the two.

Methodological insight: Test-time policy interpolation represents a lightweight, plug-and-play alignment paradigm—training an external module to modify the output distribution of an existing model. This idea transfers naturally to LLM decoding-time guidance (e.g., DExperts, contrastive decoding).

Attribute correlation perspective: Reducing violent behavior may increase deceptive behavior, highlighting that multi-objective alignment cannot optimize individual dimensions in isolation.

Scalability: Classifiers are trained and applied independently, allowing new ethical dimensions to be added without affecting existing modules.

Rating

Dimension	Score (1–5)
Novelty	3.5
Technical Depth	3
Experimental Thoroughness	4
Writing Quality	4
Value	3.5
Overall	3.5

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