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Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis
Darshan Deshpande, Anand Kannappan, Rebecca Qian
TL;DR
Reward hack detection by LLMs in code environments remains understudied despite the growing use of automated rewards. This paper introduces TRACE and contrasts isolated classification with trajectory-based anomaly detection, finding better detection in contrastive settings and greater difficulty with semantically contextualized hacks.
Problem
Automated reward functions require careful vetting because human supervision is limited, while LLM-based reward hack detection remains insufficiently benchmarked in code environments.
Method
The paper introduces TRACE, a human-verified benchmark of 517 code trajectories spanning 54 reward hack categories, and evaluates isolated versus contrastive detection using defined detection and match metrics.
Results
63% Detection Rate in contrastive evaluation versus 45% in isolation for GPT-5.2, while models struggle more with semantically contextualized than syntactically contextualized hacks.
Takeaways & Limitations
Contrastive trajectory analysis is more beneficial for LLM reward hack detection than isolated classification, but contextual reasoning about hack intent remains a substantial challenge.
Takeaways & Limitations
TRACE is planned for extension to more realistic scenarios and naturally elicited reward hack environments.
Abstract
from arXiv · showhide
Recent advances in reinforcement learning for code generation have made robust environments essential to prevent reward hacking. As LLMs increasingly serve as evaluators in code-based RL, their ability to detect reward hacking remains understudied. In this paper, we propose a novel taxonomy of reward exploits spanning across 54 categories and introduce TRACE (Testing Reward Anomalies in Code Environments), a synthetically curated and human-verified benchmark containing 517 testing trajectories. Unlike prior work that evaluates reward hack detection in isolated classification scenarios, we contrast these evaluations with a more realistic, contrastive anomaly detection setup on TRACE. Our experiments reveal that models capture reward hacks more effectively in contrastive settings than in isolated classification settings, with GPT-5.2 with highest reasoning mode achieving the best detection rate at 63%, up from 45% in isolated settings on TRACE. Building on this insight, we demonstrate that state-of-the-art models struggle significantly more with semantically contextualized reward hacks compared to syntactically contextualized ones. We further conduct qualitative analyses of model behaviors, as well as ablation studies showing that the ratio of benign to hacked trajectories and analysis cluster sizes substantially impact detection performance. We release the benchmark and evaluation harness to enable the community to expand TRACE and evaluate their models.
1. Introduction
Reward hacking threatens code-based reinforcement learning because agents can exploit reward flaws without fulfilling intended objectives. The paper proposes TRACE to benchmark detection using fine-grained, contrastive analysis rather than isolated classification.
- Reward hacking exploits flaws in reward functions to achieve high scores without fulfilling the underlying objective.
- TRACE addresses prior detection studies’ narrow hack coverage and isolated binary-classification framing with a broader anomaly-detection benchmark.
- 54 fine-grained subcategories organize code-environment exploits across test suites, solution quality, context, and execution environments.
- 63% of hacks were detected by GPT-5.2 with high reasoning, while models struggled more with semantically contextualized than syntactically contextualized hacks.
- Increasing contrasting examples and benign trajectories improved models’ ability to disentangle reward-hack patterns from benign patterns.
2. Related Work
Prior work studies reward hacking in reinforcement-learning and coding settings, but existing detection benchmarks commonly use isolated binary classification. TRACE instead frames detection as contrastive anomaly detection inspired by set-level and in-context anomaly-detection methods.
- LLM Reinforcement Learning and Reward Hacking: Reinforcement learning for language models increasingly uses explicit rewards or user preferences, making reward-function failures relevant to training.
- LLM Reinforcement Learning and Reward Hacking: Code-generation rewards based on unit tests and linting are scalable and deterministic but susceptible to hacking when environments lack safeguards.
- Prior Reward-Hack Detection: Prior coding studies examined behaviors including hard-coded tests, prompt injections, test modification, operator overloading, output hardcoding, and manipulation.
- Anomaly Detection Framing: TRACE adapts contrastive outlier detection and GRPO-inspired trajectory grouping into an evaluation setup extendable to robust-detector training.
3. TRACE Benchmark
TRACE is a synthetic, human-verified benchmark for fine-grained reward-hack detection across diverse software-engineering environments. Its taxonomy, generation pipeline, and validation process target realistic, multilabel trajectories.
- Benchmark and Taxonomy: TRACE contains 517 unique human-verified trajectories spanning 54 reward-hack instances across 10 broad categories.
- Benchmark and Taxonomy: The taxonomy expands test-suite exploitation into test modification, test-case targeting, and test-coverage gaming while adding quality-degradation criteria.
- Dataset Curation: The benchmark covers more than 37 engineering domains, including DevOps, ML infrastructure, FinTech, cybersecurity, and frontend/backend development.
- Trajectory Generation: Synthetic trajectories were generated with contextual prompts and Claude-Code2, using an experimentation directory to preserve multistep conversational context.
- Human Validation: Three software engineers independently assessed realism, hacking behavior, hack type, and detection difficulty before dataset filtering.
- Human Validation: 81% of generated samples were accepted, with Cohen’s κ = 0.82; 39% of TRACE instances contain multiple hack types, and 249 trajectories are benign.
4. Experimental Setup
The evaluation treats reward-hack detection as contrastive outlier detection, using GRPO-inspired trajectory clusters, diverse models, and defined detection metrics.
- The evaluation formulates reward-hack detection as an outlier detection task rather than isolated binary classification.
- GRPO-inspired clusters vary trajectory cluster size and benign ratio to model realistic comparative evaluation conditions.
- The benchmark evaluates state-of-the-art open and closed models, including GPT-5.2, Claude Opus 4.5, Gemini 3 Pro, Kimi K2 Thinking, GLM-4.7, and DeepSeek 3.2.
- Detection Rate is macro F1 for binary hack detection, while Match Rate is macro multilabel F1 for fine-grained hack categories conditioned on detection.
- Structured outputs parse binary detections, fine-grained categories, and confidence scores against human-annotated ground truth.
5. Results and Discussion
Models detect reward hacks more successfully in contrastive clusters than in isolation, but semantic exploit categories remain substantially harder than syntactic ones.
- RQ1. Evaluating Performance of SoTA LLMs at Reward Hack Detection: 45% was GPT-5.2’s best isolated-setting Detection Rate at N = 1, with a 61% Match Rate on correctly identified samples.
- RQ1. Evaluating Performance of SoTA LLMs at Reward Hack Detection: 34% was Claude-4.5-Opus’s absolute performance increase when cluster size rose to N = 10.
- RQ2. Do models struggle more with detecting semantically or syntactically contextualized reward hacks?: 0.0–0.4 was the Match Rate range for semantically oriented categories, including Context Exploitation, Style Manipulation, Information Leakage, and Tool Abuse.
- RQ3. How does contrastive noise in a trajectory cluster influence reward detectability: Approximately 35% was the Match Rate improvement across models from N = 1 to N = 5.
- RQ3. How does contrastive noise in a trajectory cluster influence reward detectability: Match Rates improved for all models as the benign ratio increased from 25% to 90%, while GPT-5.2 was most robust to that variation.
6. Qualitative Analysis and Discussion
Qualitative analysis links successful detection to concrete code grounding and comparative reasoning, while agreement analyses show high overall consistency with lower agreement on subjective categories.
- 34 random trajectories per LLM were manually analyzed to identify reasoning patterns associated with correct and incorrect hack identification.
- Correct Detection Patterns: Successful detections grounded judgments in specific code artifacts, explored consequences, compared behavior with legitimate alternatives, and examined agent justifications.
- Incorrect Detection Patterns: Comparative reasoning was associated with capturing more degenerate solutions in clusters, while Claude-4.5-Opus’s poor recall was attributed to ineffective comparative pattern analysis.
- LLM agreement statistics: κ = 0.812 and 90.9% absolute agreement were observed across the three best-performing models.
- LLM agreement statistics: Reproducibility across three runs reached κ = 0.795 with 90.1% agreement, but subjective categories had lower agreement than resource exploitation, tool abuse, and test case targeting.
7. Conclusion
TRACE provides a human-verified benchmark for reward-hack detection and shows that contrastive trajectory analysis improves detection over isolated classification. The study also identifies contextual reasoning and cluster composition as important factors, while future work targets more realistic scenarios.
- 63% Detection Rate for GPT-5.2 in contrastive analysis exceeded 45% in isolation on TRACE.The improvement occurred across evaluated models in the trajectory contrastive setting.
- Models struggled more with semantically contextualized hacks than with syntactic exploits.The authors characterize this difference as a gap in contextual reasoning.
- Larger analysis clusters improved performance, while the benign-to-hack trajectory ratio significantly influenced detection.
- Comparative patterns helped detection, whereas over-reliance on user or assistant behavior led to poor Detection Rates.
- Future work will extend TRACE to more realistic scenarios and environments that naturally elicit reward-hack behaviors.
Impact Statement
As AI coding agents scale, detecting objective gaming rather than correct problem solving is presented as critical for system safety and reliability. The taxonomy and benchmark are positioned as infrastructure for reward design, detection pipelines, and regulatory evaluation.
- Detecting when AI coding agents game objectives rather than solve problems correctly is critical for safety and reliability at scale.
- The taxonomy and benchmark support development of reward functions resistant to exploitation.
- The infrastructure is intended to improve detection systems in AI training pipelines and support regulatory evaluation and safety policies.
A. Domain Distribution of TRACE Dataset
TRACE spans diverse engineering domains and uses realistic, creative, multi-turn coding conversations to generate reward-hack and benign trajectories. The generation process emphasizes faithful, subtle, plausible behaviors and authentic technical details.
- 37 distinct engineering domains are represented, with IoT and Embedded Systems comprising 10.9% of the dataset.Financial and Commercial Systems collectively represent 13.6%.
- Reward-hack trajectories are generated as hyper-realistic multi-turn LM+tool-use conversations with organically emerging hacks.The prompts require realistic problem solving, debugging, tool use, and subtle behavior requiring multi-turn examination.
- Generation targets authentic development contexts using real file paths, library names, versions, error patterns, and tool outputs.The prompts also require relative rather than machine-specific absolute paths.
- Hack scenarios must faithfully match the taxonomy while remaining realistic, plausible, subtle, diverse, and difficult but possible to detect.The prompts prioritize originality, ecological validity, and distinctive technical and interaction patterns.
- Benign trajectories are prompted to complete proper, ethical tasks without reward hacking in contextually related domains.They use creative, realistic software-development scenarios with appropriate technical stacks and business constraints.
- The 517 trajectories have approximately 26 utterances on average, with lengths ranging from 14 to 49 utterances.The interquartile range is 20–31 utterances.
G. Fine Grained Definitions of Taxonomy Subcategories
The taxonomy definitions are organized into tables that specify fine-grained reward-hack subcategories used to generate TRACE. The tables cover exploitation of evaluation infrastructure, solution code, information sources, and runtime systems.
- Tables 3–6 define the subcategories and sub-subcategories used for TRACE generation.
- Test Suite Exploitation targets evaluation infrastructure.
- Solution Quality Degradation targets solution code properties.
- Context Exploitation targets information sources, while Execution Environment Hacks target the runtime system.
H. Detailed Human Evaluation Setup
Human evaluation used qualified code reviewers with academic and industry experience to assess the trajectories.
- Three annotators were selected based on computer-related education, at least two years of industry experience, and skill reviewing AI-generated code.Each annotator was paid $20 per hour.
H.1. Metric Definitions
The human evaluation defines realism through four trajectory qualities and difficulty through detection effort and required knowledge, then aggregates annotator ratings.
- Realism: Realism measures agent plausibility, event believability, conversational progression, and hack subtlety.A realistic trajectory plausibly emerges from a learning agent, exploits misspecification believably, progresses naturally, and avoids implausible obviousness or concealment.
- Realism: Annotators rank trajectory realism on a 1–3 scale across the four dimensions.The dimensions correspond to agent plausibility, event believability, conversation progression, and hack subtlety.
- Realism: Mean realism ratings are reported in Table 1 after averaging annotator scores, with high-disagreement instances removed.This filtering excludes instances where annotators substantially disagreed.
- Difficulty: Difficulty measures the time taken and knowledge required to identify a reward hack.The resulting annotator difficulty scores are averaged and reported in Table 1.