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Steerability of Instrumental-Convergence Tendencies in LLMs

Jakub Hoscilowicz

arXiv:2601.01584v2cs.CL

TL;DR

Whether capability growth reduces steerability matters because control requires builders to enforce intended behavior, while security requires limiting attackers’ ability to elicit disallowed behavior. The paper measures prompt-suffix steering across Qwen3 variants on InstrumentalEval. It finds large shifts in convergence rates, with lower rates under anti-instrumental prompting for larger aligned variants in this setup.

  • Problem

    The paper asks whether increasing capability makes AI systems harder to steer, a question tied to authorized control and unauthorized elicitation in open-weight models.

    Source: §1 · pp. 1–2, §4 · p. 6

  • Method

    The study compares pro- and anti-instrumental prompt suffixes across Qwen3 variants using InstrumentalEval, a fixed response-labeling setup, and convergence/refusal measures.

    Source: §1 · p. 2, §3.1 · p. 4, §3.3 · p. 4

  • Results

    4.23% → 2.82% (Instruct) and 9.86% → 4.23% (Thinking) were the anti-instrumental convergence rates associated with scaling from 4B to 30B.

    Source: §3.3 · p. 5

  • Takeaways & Limitations

    Open-weight models face a safety–security tension: similar steering interventions can support safer behavior for benign operators and elicit disallowed behavior for malicious users.

    Source: §4 · p. 6, §6 · p. 8

    InstrumentalEval has 76 scenarios, judgments are automatic, and measured rates may depend on prompt wording; the experiments test simple prompt suffixes rather than more sophisticated steering methods.

    Source: §5 · p. 7

Abstract

from arXiv · show

We examine two properties of AI systems: capability (what a system can do) and steerability (how reliably one can shift behavior toward intended outcomes). A central question is whether capability growth reduces steerability and risks control collapse. We also distinguish between authorized steerability (builders reliably reaching intended behaviors) and unauthorized steerability (attackers eliciting disallowed behaviors). This distinction highlights a fundamental safety--security dilemma of AI models: safety requires high steerability to enforce control (e.g., stop/refuse), while security requires low steerability for malicious actors to elicit harmful behaviors. This tension presents a significant challenge for open-weight models, which currently exhibit high steerability via common techniques like fine-tuning or adversarial attacks. Using Qwen3 and InstrumentalEval, we find that a short anti-instrumental prompt suffix sharply reduces the measured convergence rate (e.g., shutdown avoidance, self-replication). For Qwen3-30B Instruct, the convergence rate drops from 81.69% under a pro-instrumental suffix to 2.82% under an anti-instrumental suffix. Under anti-instrumental prompting, larger aligned models show lower convergence rates than smaller ones (Instruct: 2.82% vs. 4.23%; Thinking: 4.23% vs. 9.86%). Code is available at github.com/j-hoscilowicz/instrumental_steering.

1 Introduction

The introduction treats the possibility that rising capability reduces steerability as an empirical question, not a default assumption. It frames the inquiry around capability, steerability, and the safety–security dilemma for open-weight models.

1 Introduction

  • The paper tests whether greater capability necessarily reduces steerability, rather than assuming a negative relationship.It contrasts the control-collapse hypothesis with evidence that newer models and steering interventions can remain responsive to user intent and constraints.Source: §1 · pp. 1–2
  • The inquiry distinguishes compatibility, control-collapse, and safety–security trade-off hypotheses.These ask whether high capability can coexist with steerability, whether interventions become brittle as capability and agency grow, and whether authorized and unauthorized steerability remain close.Source: §1 · pp. 1–2
  • Steerability is measured protocol-relatively as the shift in outputs caused by feasible short prompt suffixes under fixed elicitation and scoring.The study compares pro- and anti-instrumental suffixes on InstrumentalEval and reports convergence and refusal rates plus a steerability gap.Source: §1 · p. 2

2 Framework

The framework treats control-collapse mechanisms as hypotheses and centers observable capability and protocol-relative steerability. It distinguishes authorized from unauthorized interventions and cautions against inferring internal mental states from labeled outputs.

2.1 Candidate Mechanisms for Control Collapse

  • Instrumental convergence, Omohundro drives, optimization pressure, and mesa-optimization motivate measuring whether instrumental-like behavior can be suppressed or amplified by feasible interventions.The framework presents these mechanisms as hypotheses rather than established explanations.Source: §2.1 · p. 2

2.2 Intelligence and Capability

  • Capability is treated as observable performance under an evaluation protocol, while labels such as deception and shutdown avoidance are output clusters, not evidence of internal intent.The authors remain agnostic about latent mechanics because multiple internal explanations can fit the same observations.Source: §2.2 · p. 3

2.3 Operationalization of Steerability

  • Steerability means how effectively engineering interventions change behavior, with authorized steering reaching intended behaviors and unauthorized steering eliciting disallowed ones.Interventions may act at training time or deployment time; the framework rejects inevitability of steerability collapse without claiming control is guaranteed.Source: §2.3 · p. 3

3 Experiments

The experiments use InstrumentalEval to test prompt-suffix steering across Qwen3 variants. Short suffixes shift convergence outcomes, while the results also show differences by post-training and a safety–security tension for open-weight models.

3.1 Benchmark

  • InstrumentalEval contains 76 hand-crafted scenarios across six categories, and evaluates whether short suffixes amplify or suppress instrumental-convergence markers.A judge labels responses as convergence, no convergence, safety refusal, or nonsensical; the steerability gap is the pro-minus-anti convergence rate.Source: §3.1 · pp. 3–4

3.2 Models

  • The evaluated Qwen3 variants are Base, Instruct, and Thinking models at 4B and 30B sizes.Thinking variants are described as optimized for longer-horizon reasoning and as a closer proxy for agentic-style systems.Source: §3.2 · p. 4

3.3 Results

  • 78.87 pp (30B Instruct) and 67.60 pp (4B Instruct) were the largest steerability gaps; Thinking gaps were 71.83 pp (4B) and 69.01 pp (30B).Across variants, pro-instrumental prompting increased convergence, while anti-instrumental prompting reduced aligned variants to low single-digit rates.Source: §3.3 · pp. 4–5, Table 1 · p. 5
  • 4.23% → 2.82% (Instruct) and 9.86% → 4.23% (Thinking) accompanied 4B-to-30B scaling; higher anti-instrumental refusal rates for 30B aligned variants indicate refusals mediated some suppression.The reported scaling relationship is an association, not a causal claim.Source: §3.3 · p. 5

3.3.1 Trade-Off

  • The same short-suffix interventions can steer open-weight models toward safer behavior or toward disallowed behavior, creating a safety–security tension.The paper notes that real-world attacker methods include a broader set of interventions than the prompt suffixes tested here.Source: §3.3.1 · p. 5

4 Implications for Open-Weight Models

Open-weight models face a safety–security dilemma: builders benefit from steering models toward intended behavior, while weight access expands attackers’ ability to elicit disallowed behavior. The section surveys mitigations and their practical boundaries.

4 Implications for Open-Weight Models

  • Releasing weights expands the attacker set and can increase unauthorized steerability, while safety still benefits from builders reliably steering models toward intended behavior.The proposed levers include capability removal, tamper resistance, and interface restriction.Source: §4 · p. 6
  • Capability unlearning aims to remove a hazard-specific latent ability while preserving general-task utility, making that hazard harder to elicit if the excision succeeds.The passage gives bio-weapon synthesis as an example.Source: §4 · p. 6
  • Homomorphic encryption could protect model parameters during inference, but its inference remains orders of magnitude slower than plaintext and is viewed as impractical for large generative models.Source: §4 · p. 6
  • A tamper-resistant checkpoint seeks to frustrate small downstream fine-tuning attempts, but feasibility is moderate, making it a deterrent rather than an ultimate defense.Attacker-driven updates may also cause broad capability degradation by exploiting sharp curvature or poor conditioning.Source: §4 · pp. 6–7
  • API-only deployment is the only widely deployed way described to retain high authorized steerability while reducing unauthorized steering through direct weight access.Providers retain control of weights and inference, can monitor misuse, and can update mitigations; black-box prompting attacks remain possible.Source: §4 · p. 7

5 Limitations

The experiments use a small, automatically judged scenario set and simple prompt-suffix steering, so the reported measurements have limited scope. The authors emphasize large, robust shifts rather than small rate changes.

5 Limitations

  • The evaluation covers 76 scenarios, uses an automatic judge, and may be sensitive to non-conceptual prompt wording.Source: §5 · p. 7
  • Differences of a few percentage points should be read as no clear shift under this protocol, with large robust shifts forming the main signal.Source: §5 · p. 7
  • The experiments test the simplest steering technique—prompt suffixes—and leave systematic evaluation of post-training and representation-level interventions to future work.The authors note that such methods could increase or decrease measured convergence rates.Source: §5 · p. 7

6 Conclusion

The conclusion treats steerability as protocol-relative and separates authorized control from unauthorized elicitation. In the tested regime, minimal prompt suffixes strongly shift instrumental-convergence outputs, while preventing unauthorized steering remains an open problem for open-weight models.

6 Conclusion

  • Steerability is measured by how strongly feasible interventions shift behavior under a stated elicitation and scoring setup, and is divided into authorized and unauthorized forms.Source: §6 · p. 7
  • For open-weight systems, the same mechanisms that enable builder control can also enable adversarial misuse.Source: §6 · p. 7
  • Minimal inference-time prompt suffixes can strongly suppress or amplify outputs labeled as instrumental convergence under the evaluation protocol.Source: §6 · pp. 7–8
  • In this regime, increased capability does not imply weaker control; aligned variants remain responsive to constraint prompts, while preventing unauthorized steering remains a major open technical problem.Source: §6 · p. 8
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