Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TTS strategies have historically been handcrafted, relying heavily on human intuition to dictate the rules of the model’s reasoning.
To address this bottleneck, researchers from Meta, Google, and several universities have introduced AutoTTS, a framework that automatically discovers optimal TTS strategies. This automated approach allows enterprise organizations to dynamically optimize compute allocation without manually tuning heuristics.
By implementing the optimal strategies discovered by AutoTTS, organizations can directly reduce the token usage and operational costs of deploying advanced reasoning models in production environments. In experimental trials, AutoTTS managed inference budgets efficiently, successfully reducing token consumption by up to 69.5% without sacrificing accuracy.
Test-time scaling enhances LLMs by granting them extra compute when generating answers. This extra compute allows the model to generate multiple reasoning paths or evaluate its intermediate steps before arriving at a final response.
The primary challenge for designing TTS strategies is determining how to allocate this extra computation optimally. Historically, researchers have designed these strategies manually, relying on guesswork to build rigid heuristics. Engineers must hypothesize the rules and thresholds for when a model should branch out into new reasoning paths, probe deeper into an existing path, prune an unpromising branch, or stop reasoning altogether.
Because this manual tuning process is constrained by human intuition, a vast amount of possible approaches remain unexplored. This often results in suboptimal trade-offs between model accuracy and computing costs.
Current TTS algorithms can be mapped to a width-depth control space — "width" being the number of reasoning branches explored, "depth" being how far each develops. Self-consistency (SC) samples a fixed number of trajectories and majority-votes the answer. Adaptive-consistency (ASC) saves compute by stopping early once a confidence threshold is hit. Parallel-probe takes a more granular approach, pruning unpromising branches while deepening the rest. All three are hand-crafted, and that's the constraint AutoTTS is designed to break.
While some more advanced methods employ richer structures like tree search or external verifiers, they all share one key characteristic: they are meticulously hand-crafted. This manual approach restricts the scope of strategy discovery, leaving a massive portion of the potential resource-allocation space untouched.
AutoTTS reframes the way test-time scaling is optimized. Instead of treating strategy design as a human task, AutoTTS approaches it as an algorithmic search problem within a controlled environment.
This framework redefines the roles of both the human engineer and the AI model. Rather than hand-crafting specific rules for when an LLM should branch, prune, or stop reasoning, the engineer's role shifts to constructing the discovery environment. The human defines the boundaries, including the control space of states and actions, optimization objectives balancing accuracy versus cost, and the specific feedback mechanisms.
AutoTTS framework (source: arXiv)
An explorer LLM, such as Claude Code, designs the strategy. This explorer acts as an autonomous agent that iteratively proposes TTS “controllers.” These controllers are code-defined policies or algorithms that dictate how an AI model allocates its computational budget during inference. The explorer tests and refines these controllers based on feedback until it discovers an optimal resource-allocation policy.
To make this automated search computationally affordable, AutoTTS relies on an “offline replay environment.” If the explorer LLM had to invoke a base reasoning model to generate new tokens every time it tested a new strategy, the compute costs would be astronomical. Instead, it relies on thousands of reasoning trajectories pre-collected from the base LLM. These trajectories include "probe signals," which are intermediate answers that help the controller evaluate progress across different reasoning branches.
During the discovery loop, the explorer agent proposes a controller and evaluates it against this offline data. The agent observes the execution traces of the proposed controller that show it allocated compute over time. By analyzing these traces, the agent can diagnose specific failure modes, such as noting if a controller pruned branches too aggressively in a specific scenario. This provides an advantage over just viewing a final result. The agent then iteratively rewrites its code to improve the accuracy-cost tradeoff.
Because the explorer agent is not constrained by human intuition, it can discover highly coordinated, complex rules that a human engineer would likely never hand-code. One optimal controller discovered by AutoTTS, named the Confidence Momentum Controller, leverages several non-obvious mechanisms to manage compute:
Trend-based stopping: Hand-crafted strategies often instruct the model to stop reasoning once it hits a certain instantaneous confidence threshold. The AutoTTS agent discovered that instantaneous confidence can be misleading due to temporary spikes. Instead, the controller tracks an exponential moving average (EMA) of confidence and only stops if the overall confidence level is high and the trend is not actively declining.
Coupled width-depth control: Manually designed algorithms usually treat the "widening" of new reasoning paths and the "deepening" of current paths as separate decisions. AutoTTS discovered a closed feedback loop where the two actions are linked. If the confidence of the current branches stalls or regresses, the controller automatically triggers the spawning of new branches.
Alignment-aware depth allocation: Instead of giving all active reasoning branches an equal computation budget, the controller dynamically identifies which branches agree with the current leading answer. It then gives those branches priority "bursts" of extra computation. This concentrates the computational budget on the emerging consensus to quickly verify if it is correct.
To test whether an AI could autonomously discover a better test-time scaling strategy, researchers set up a rigorous evaluation framework. The core experiments were conducted on Qwen3 models ranging from 0.6B to 8B parameters. The researchers also tested the system's ability to generalize on a distilled 8B version of the DeepSeek-R1 model.
The explorer AI agent was initially tasked with discovering an optimal strategy using the AIME24 mathematical reasoning benchmark. This discovered strategy was then tested on two held-out math benchmarks, AIME25 and HMMT25, as well as the graduate-level general reasoning benchmark GPQA-Diamond.
The AutoTTS discovered controller was pitted against four manually designed test-time scaling algorithms in the industry. These baselines included Self-Consistency with 64 parallel reasoning paths (SC@64), Adaptive-Consistency (ASC), Parallel-Probe, and Early-Stopping Self-Consistency (ESC). ESC is a hybrid approach that generates trajectories in parallel and stops early when an answer seems stable.
AutoTTS (red line) outperforms other baselines on industry benchmarks (source: arXiv)
When set to a balanced, cost-conscious mode, the AutoTTS-discovered controller reduced total token consumption by approximately 69.5% compared to SC@64. At the same time, the controller maintained the same average accuracy across the four Qwen models. When the inference budget was turned up, AutoTTS pushed peak accuracy beyond all handcrafted baselines in five out of eight test cases.
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This efficiency translated to other tasks. On the GPQA-Diamond benchmark, the balanced AutoTTS variant slashed the inference token cost from 510K tokens down to just 151K tokens, while slightly improving overall accuracy. On the DeepSeek model, AutoTTS achieved the highest overall accuracy on the HMMT25 benchmark while cutting the token spend nearly in half.
For practitioners building enterprise AI applications, these experiments highlight two major operational benefits:
Raising peak performance: AutoTTS doesn't just save money on token consumption. It actively raises the peak attainable performance of the base model. The AI-designed controller is remarkably good at detecting noisy or unproductive reasoning branches on the fly and continuously redirecting its compute budget toward the branches generating the most useful reasoning signals.
Cost-effective custom development: Because the framework relies on an offline replay environment, the entire discovery process cost only $39.90 and took 160 minutes. For enterprise teams, that means optimized reasoning strategies tailored to proprietary models and internal tasks are now within reach — without a dedicated research budget.
Both the AutoTTS framework and the Confidence Momentum Controller are available on GitHub; the CMC can be used as a drop-in replacement for other TTS controllers.
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