Learning a Continue-Thinking Token for Enhanced Test-Time Scaling
Authors: name: Liran Ringel, name: Elad Tolochinsky, name: Yaniv Romano

Test-time scaling has emerged as an effective approach for improving language
model performance by utilizing additional compute at inference time. Recent
studies have shown that overriding end-of-thinking tokens (e.g., replacing
"</think>" with "Wait") can extend reasoning steps and improve accuracy. In
this work, we explore whether a dedicated continue-thinking token can be
learned to trigger extended reasoning. We augment a distilled version of
DeepSeek-R1 with a single learned "<|continue-thinking|>" token, training only
its embedding via reinforcement learning while keeping the model weights
frozen. Our experiments show that this learned token achieves improved accuracy
on standard math benchmarks compared to both the baseline model and a test-time
scaling approach that uses a fixed token (e.g., "Wait") for budget forcing. In
particular, we observe that in cases where the fixed-token approach enhances
the base model's accuracy, our method achieves a markedly greater improvement.
For example, on the GSM8K benchmark, the fixed-token approach yields a 1.3%
absolute improvement in accuracy, whereas our learned-token method achieves a
4.2% improvement over the base model that does not use budget forcing.