RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments

2026-06-24 · 17:59 UTC ·Babak Rahmani, Sebastian Dziadzio, Joschka Strüber... ·1 min read

Brief

RevengeBench frames the inverse problem of recovering executable decision programs from behavioral traces: learners watch a hidden policy play, design controlled opponent probes, and return code hypotheses evaluated by continuous action-distance and tournament performance. The benchmark uses 75 LLM-generated, Elo-calibrated policies across five games; experiments on 12 LLMs show 34–72% recovery and tangible competitive improvements, highlighting applications to opponent modeling and interpretability.

Why it matters

RevengeBench (Rahmani et al., arXiv 2026-06-24) provides a benchmark of 75 LLM-generated, Elo-calibrated target policies across five game environments (from CodeClash trajectories); learners observe matches, design custom opponent probes, and submit executable code hypotheses scored with continuous action-distance metrics.

Key details

Across twelve frontier LLMs, recovery quality ranged from 34% to 72% of the initial action-distance closed; reconstructed policies produced measurable competitive gains in downstream player-vs-player tournaments, especially improving weaker models' ability to craft counter-strategies.

Abstract

For most of scientific history, researchers studying behavior could only infer hidden mechanisms from outward actions: an inverse problem that becomes more tractable when observation is augmented by targeted intervention. We pose a computational analogue: given only behavioral traces of an agent in a game environment, can a learner reconstruct the underlying decision program as executable code, and how much does this reconstruction improve with the ability to design controlled experiments? We introduce RevengeBench, a benchmark of 75 LLM generated, Elo-calibrated policies across five game environments, drawn from CodeClash tournament trajectories. The learner observes the hidden target policy play against sampled opponents and designs behavioral probes in the form of custom opponent policies that elicit informative behavior. It then submits an executable hypothesis, which is evaluated using continuous action-distance metrics. We further validate that recovered code carries informative signal in downstream player-versus-player tournaments. Across twelve frontier LLMs, recovery quality varies substantially (34 to 72% of initial distance closed), with reconstructed policies yielding measurable competitive advantage, particularly for weaker models that otherwise struggle to design effective counter-strategies. Our benchmark positions behavioral recovery of programmatic policies as a tractable inverse problem in code-space, opening a path to opponent modeling, policy interpretability, and the broader question of inferring latent mechanisms from observations.

Comment: 12 pages, 5 figures, 22 appendix pages