Test-time scaling improves the reasoning capability of large language models by generating longer sequential chains of thought. While effective, this approach reaches a bottleneck as computation increases, resulting in a “test-time scaling plateau” where further computation provides only marginal gains. We argue that this ceiling reflects a limitation of the scaling strategy rather than model capability, a phenomenon we term Tunnel Vision, where imperfect early steps can lock the model into a suboptimal reasoning path. Motivated by this observation, we propose ParaThinker, an end-to-end parallel reasoning framework that trains LLMs to generate multiple reasoning paths in parallel and aggregate them into a final answer within a single architecture. By exploring different lines of thought, ParaThinker sidesteps the Tunnel Vision issue and unlocks the model’s reasoning potential. On challenging reasoning benchmarks, ParaThinker achieves accuracy improvements (e.g., 6.5%–20.7% on AIME-24 with a 1.5B model) over sequential-reasoning LLMs under the same budget of decoding tokens. Under matched decoding token budgets and with KV-cache reuse, ParaThinker achieves 38.7%–66.8% lower inference latency than sequential decoding via batched parallel generation. The code is available at https://github.com/MobileLLM/ParaThinker.

Scaling test-time computation improves the reasoning ability of large language models (LLMs), but the resulting latency and cost limit its use in latency- and cost-sensitive scenarios. We present BudgetThinker, a framework for reasoning-budget adherence that provides online control of chain-of-thought (CoT) length during decoding. Rather than relying on a one-shot prompt instruction, BudgetThinker periodically inserts specialized tokens that communicate the remaining reasoning budget. A two-stage training pipeline, consisting of supervised fine-tuning and curriculum-based reinforcement learning, teaches the model to interpret these signals and adapt its reasoning pace. Across the evaluated 1.5B and 7B text backbones, a 7B multimodal backbone, and a 1.5B code-reasoning model evaluated on HumanEval, BudgetThinker improves budget adherence and accuracy–efficiency trade-offs over competitive baselines. The controlled comparisons isolate the contribution of online ratio signaling from the shared training pipeline. Code is available at https://github.com/MobileLLM/BudgetThinker.