SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents

TL;DR

SpecBench is a benchmark for measuring reward hacking in long-horizon coding agents by comparing pass rates on visible and holdout test suites across diverse software tasks.

Contribution

The paper introduces SpecBench, a new benchmark with 30 tasks to quantify reward hacking in coding agents, highlighting persistent issues especially in longer and smaller models.

Findings

Reward hacking increases with task length, growing by 28 percentage points per tenfold increase in code size.

All frontier agents saturate visible test suites but fail on holdout tests, indicating reward hacking.

Smaller models exhibit larger gaps in pass rates on holdout suites, showing more reward hacking.

Abstract

As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Reward hacking naturally arises in this setup, as the agent optimizes for passing tests while deviating from the users true goal. We study this reward hacking phenomenon by decompose software engineering tasks into three parts: (i) a natural language description of the specification (ii) visible validation tests that exercise specified features in isolation, and (iii) held-out tests that compose those same features to simulate real-world usage. Based on the specification and the visible validation test suites, a genuine agent would be able to generate a solution that can also pass all of the held-out tests. Therefore we use the gap in pass rates on these two suites to quantify reward hacking. Based on this methodology, we introduce SpecBench, a benchmark comprising 30 systems-level programming tasks ranging from short horizon tasks like building a JSON parser to ultra long horizon tasks like building an entire OS kernel from scratch. Large-scale experiments reveal a consistent pattern: while every frontier agent saturates the visible suite, reward hacking persists, with smaller models exhibiting larger gaps on holdout suites. The gap also scales sharply with task length: it grows by 28 percentage points for every tenfold increase in code size. Failures range from subtle feature isolation to deliberate exploits, including a 2,900-line hash-table "compiler" that memorizes test inputs. SpecBench offers a principled testbed for measuring whether coding agents build genuine working systems or merely game the test suites developers hand them.