InfoSteer: Steering Information Utility in Language Model Post-Training
Chunyuan Deng, Ruidi Chang, Hanjie Chen
Recent advancements in language models (LMs) gradually ushered in an era where post-training is crucial. Yet, post-training approaches such as supervised fine-tuning (SFT) do not guarantee effective use of knowledge acquired during pretraining. We therefore present InfoSteer, a lightweight method that encourages parametric information utilization in LMs during post-training. This is achieved via treating FFN layer as associate key-value memory, and promotes the use of stored memory vectors via forward-pass interventions or regularization during backpropagation. We find this simple guidance during post-training phase delivers consistent performance improvements across diverse model families--including Qwen, Gemma and Llama-spanning over 15 downstream tasks in both ID and OOD evaluations. Beyond performance gains, we also find that steered LMs can adaptively allocate information—placing more emphasis on generating semantically meaningful tokens, while using fewer resources on simple transition ones (e.g., , or and). Our work underscores that vanilla post-training does not fully leverage pre-training potential, and steering LMs in latent representation space offers a promising approach that enhances both performance and interpretability.