Opening Moves: Understanding and Steering Language Model Reasoning

A reasoning model’s first generated token can change thousands of tokens that follow. We study this sensitivity through opening memory, the keys and values cached at the opening-token position. Matched-text interventions show that exchanging opening keys or values changes output length without changing the visible opening, and that later cache entries can carry these effects under identical token histories. Based on these observations, we introduce Opening-Memory Steering (O-Steer), which steers long reasoning trajectories through a single edit to the memory written by the opening token. O-Steer uses natural opening tokens to construct a compact edit space, complete-continuation feedback to select a shared memory anchor, and a small problem-dependent controller to adapt the edit. At inference time, the frozen model’s opening memory is modified once, after which ordinary decoding proceeds without further intervention. Across 1,919 problems from six math benchmarks, O-Steer reduces mean output length by 40.3% on Qwen3-1.7B and 22.8% on DeepSeek-R1-Distill-Qwen-1.5B, with accuracy decreases of 5.91 and 4.17 percentage points, respectively. These results show that a single opening-memory edit can substantially change the accuracy–length trade-off of long reasoning trajectories.