ROAST: Rollout-based On-distribution Activation Steering Technique

TL;DR

ROAST introduces a new activation steering method for large language models that estimates directions from on-distribution rollouts, improving robustness and performance without relying on off-distribution supervision or discrete masking.

Contribution

ROAST proposes a rollout-based on-distribution activation steering technique with continuous soft scaling and grouped normalization, enhancing robustness and effectiveness over prior methods.

Findings

ROAST improves task performance across models (e.g., +9.7% on GSM8K, +12.1% on TruthfulQA).

CSS better preserves activation energy and maintains semantic quality.

Grouped normalization balances contributions, leading to more reliable steering directions.

Abstract

Activation steering provides parameter-efficient control over large language models (LLMs) at inference time, but many methods rely on off-distribution supervision and discrete masking, leading to brittle interventions. We propose ROAST (Rollout-based On-distribution Activation Steering Technique), which estimates steering directions from the model's own on-distribution rollouts via ROC and avoids hard sparsification via Continuous Soft Scaling (CSS) and Grouped Mean Normalization. Our empirical analysis reveals that while activation magnitude correlates moderately with directional consistency, the variance in magnitude is significant and often disproportionate to semantic quality. This suggests that high-magnitude activations risk dominating the global steering direction if not properly normalized. To address this, ROAST employs grouped normalization to balance contributions across samples, ensuring a more robust estimation of the consensus steering direction. Across models (0.6B to 32B), ROAST consistently improves performance on diverse tasks (e.g., +9.7% on GSM8K for Qwen3-0.6B and +12.1% on TruthfulQA for GLM4-32B), and analyses show that CSS better preserves activation energy.