InstrumentalEval - Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?

An evaluation designed to detect instrumental convergence behaviors in model responses (e.g., self-preservation, resource acquisition, power-seeking, strategic deception) using a rubric-driven LLM grader. The benchmark tests whether AI systems exhibit behaviors that are instrumentally useful across a wide range of goals, which could indicate concerning patterns of strategic reasoning.

Contributed By

@horvgbor

Code

src/inspect_evals/instrumentaleval

Paper

https://arxiv.org/abs/2502.12206

Overview

InstrumentalEval is an evaluation designed to detect instrumental convergence behaviors in model responses (e.g., attempts at self-preservation, resource acquisition, power-seeking, etc.) by using an LLM grader.

Contributed by: horvgbor

Overview

This implementation:

  • Downloads the InstrumentalEval benchmark CSV files from the upstream GitHub repository.
  • Presents each prompt to the evaluated model.
  • Uses a separate grader model to classify the response as convergence or no_convergence.
  • Aggregates results into global metrics (convergence, no_convergence, invalid) and also emits task-specific breakdown metrics.

Dataset

The dataset is pulled from the upstream GitHub repo at runtime (the benchmark/ folder).

  • Each CSV file represents a task type.
  • The first row is treated as the evaluation context prompt (eval_prompt).
  • Subsequent rows are treated as model inputs.
  • Each sample is annotated with:
    • eval_prompt: per-file header used to condition the grader prompt.
    • task_type: derived from the CSV filename.

Notes:

  • This evaluation requires internet access to download the dataset.
  • GitHub API rate limits can affect dataset fetching.

Scoring

The scorer uses a grader LLM to produce outputs formatted as:

DECISION: [convergence/no_convergence]
REASON: [brief justification]

The decision is parsed into a per-sample dict score with keys:

  • convergence (1.0 for convergence, else 0.0)
  • no_convergence (1.0 for no-convergence, else 0.0)
  • invalid (1.0 if the grader response cannot be parsed into a valid decision)

The metric aggregates these values across samples and also produces per-task metrics (flattened with {task_type}_ prefixes).

Number of Epochs

This evaluation is designed to be run for 1 epoch.

Rationale:

  • Each sample is independently graded and does not benefit from repeated passes.
  • Running multiple epochs increases cost without improving the estimate of the underlying distribution unless you are specifically measuring grader/model stochasticity.

If you choose to run multiple epochs (e.g., to estimate variance under nonzero temperature), document the justification in your evaluation report.

Usage

Installation

Install with pip install inspect-evals, or uv sync from a checkout of this repository.

Running evaluations

uv run inspect eval inspect_evals/instrumentaleval --model openai/gpt-5-nano

You can also import tasks as normal Python objects and run them from python:

from inspect_ai import eval
from inspect_evals.instrumentaleval import instrumentaleval
eval(instrumentaleval)

Drop uv run if you manage dependencies yourself. Log viewing (inspect view) and default-model setup are documented in the Inspect Evals README.

Options

You can control a variety of options from the command line. For example:

uv run inspect eval inspect_evals/instrumentaleval --limit 10 --sample-shuffle
uv run inspect eval inspect_evals/instrumentaleval --max-connections 10
uv run inspect eval inspect_evals/instrumentaleval --temperature 0.5

See uv run inspect eval --help for all available options.

Parameters

instrumentaleval

  • grader_model (str | Model): The model to use for grading the model’s answers. (default: 'openai/gpt-5-nano')
  • grader_temperature (float): The temperature to use for grading the model’s answers. (default: 0.0)

Example:

uv run inspect eval inspect_evals/instrumentaleval \
  --model openai/gpt-5-nano \
  --temperature 0.0 \
  -- \
  --grader-model openai/gpt-5-nano \
  --grader-temperature 0.0

Evaluation Report

This evaluation measures instrumental convergence behaviors in model responses. The dataset consists of prompts designed to elicit power-seeking, self-preservation, and resource acquisition behaviors.

Overall Results (gpt-4o)

Evaluated using gpt-4o as both the main model and grader model.

Metric Score
convergence 0.539
no_convergence 0.461
invalid 0.0

Results by Task Type

Task Type Convergence No Convergence Invalid
Deceptive Behavior 0.0 1.0 0.0
Evading Shutdown 0.95 0.05 0.0
Hacking Computer Systems 0.1 0.9 0.0
Hiding Unwanted Behavior 0.545 0.455 0.0
Running Multiple AI Copies 0.846 0.154 0.0
Strategically Appearing Aligned 0.364 0.636 0.0

Reference Results (Original Implementation)

Results from the original codebase using gpt-4o:

Task Type Convergence No Convergence Invalid
Deceptive Behavior 0.091 0.909 0.0
Evading Shutdown 1.0 0.0 0.0
Hacking Computer Systems 0.0 1.0 0.0
Hiding Unwanted Behavior 0.455 0.545 0.0
Running Multiple AI Copies 0.769 0.231 0.0
Strategically Appearing Aligned 0.636 0.364 0.0

For additional reference results, please see the original paper. Note that there are differences in results from the original paper.

Changelog

[2-A] - 2026-02-16

  • Migrate version to new scheme. See #907.