Tag
LARA is a method for efficient adaptation that adds low-rank corrections to a frozen model's residual stream instead of modifying weights, matching LoRA's performance while enabling composable behaviors and inference-time steering.
This paper introduces RL^2, an adaptive inference-time steering framework for Vision-Language-Action models that uses offline RL on latent representations to compose action flows, activating steering only when failure is predicted. It achieves up to +17.3% success rate improvements on SIMPLER and PolaRiS benchmarks and demonstrates real-world transfer.
GrAInS is a contrastive gradient-based method that uses Integrated Gradients to identify influential tokens and construct steering vectors for inference-time steering of LLMs and VLMs, improving truthfulness and reducing hallucinations without degrading fluency.
QPILOTS is a method that steers flow policies at inference time by using critic gradients projected from noisy intermediate states, achieving state-of-the-art performance on offline-to-online RL benchmarks and improving pretrained VLA models without modifying the base policy.
FineSteer is a novel inference-time steering framework that decomposes steering into conditional steering and fine-grained vector synthesis stages, using Subspace-guided Conditional Steering (SCS) and Mixture-of-Steering-Experts (MoSE) mechanisms to improve safety and truthfulness while preserving model utility. Experiments show 7.6% improvement over state-of-the-art methods on TruthfulQA with minimal utility loss.