TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models

• URL: http://arxiv.org/abs/2604.14847v1
• Date: Thu, 16 Apr 2026 10:33:00 GMT
• Title: TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models
• Abstract summary: Large Reasoning Models (LRMs) achieve strong performance on complex tasks but suffer from high inference latency due to autoregressive reasoning.<n>Recent work explores using Small Reasoning Models (SRMs) to accelerate LRM inference.<n>TrigReason is a trigger-based collaborative reasoning framework that replaces continuous polling with selective intervention.
• Score: 59.83398270447905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks through extended chains of thought but suffer from high inference latency due to autoregressive reasoning. Recent work explores using Small Reasoning Models (SRMs) to accelerate LRM inference. In this paper, we systematically characterize the capability boundaries of SRMs and identify three common types of reasoning risks: (1) path divergence, where SRMs lack the strategic ability to construct an initial plan, causing reasoning to deviate from the most probable path; (2) cognitive overload, where SRMs fail to solve particularly difficult steps; and (3) recovery inability, where SRMs lack robust self-reflection and error correction mechanisms. To address these challenges, we propose TrigReason, a trigger-based collaborative reasoning framework that replaces continuous polling with selective intervention. TrigReason delegates most reasoning to the SRM and activates LRM intervention only when necessary-during initial strategic planning (strategic priming trigger), upon detecting extraordinary overconfidence (cognitive offload trigger), or when reasoning falls into unproductive loops (intervention request trigger). The evaluation results on AIME24, AIME25, and GPQA-D indicate that TrigReason matches the accuracy of full LRMs and SpecReason, while offloading 1.70x - 4.79x more reasoning steps to SRMs. Under edge-cloud conditions, TrigReason reduces latency by 43.9\% and API cost by 73.3\%. Our code is available at \href{https://github.com/QQQ-yi/TrigReason}{https://github.com/QQQ-yi/TrigReason}

Related papers

• TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping [5.190961793309368]
  We introduce TRACES, a lightweight framework that tags reasoning steps in real-time.<n>We find that LRMs tend to shift their reasoning behavior after reaching a correct answer.<n>We demonstrate that the monitoring of the specific type of steps can produce effective interpretable early stopping criteria.
  arXiv  Detail & Related papers  (2026-04-22T20:00:18Z)
• RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning [10.68221932401407]
  Large reasoning models (LRMs) incur substantial inference latency and computational overhead.<n>We propose RankGuide, a framework that improves the efficiency and effectiveness of collaboration through tensor-rank-guided steering.<n>RankGuide reduces latency by up to $1.75times compared to LRM, while maintaining competitive accuracy relative to prior methods.
  arXiv  Detail & Related papers  (2026-04-17T20:51:04Z)
• When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning [52.21239821135325]
  We introduce Dynamic Thought Sufficiency in Reasoning (DTSR), a novel framework for efficient reasoning.<n>Inspired by human metacognition, DTSR operates in two stages: Reflection Signal Monitoring, and Thought Sufficiency Check.<n>DTSR reduces reasoning length by 28.9%-34.9% with minimal performance loss, effectively mitigating overthinking.
  arXiv  Detail & Related papers  (2026-04-08T07:56:28Z)
• APR: Penalizing Structural Redundancy in Large Reasoning Models via Anchor-based Process Rewards [61.52322047892064]
  Test-Time Scaling (TTS) has significantly enhanced the capabilities of Large Reasoning Models (LRMs)<n>We observe that LRMs frequently conduct repetitive self-verification without revision even after obtaining the final answer during the reasoning process.<n>We propose Anchor-based Process Reward (APR), a structure-aware reward shaping method that localizes the reasoning anchor and penalizes exclusively the post-anchor AST.
  arXiv  Detail & Related papers  (2026-01-31T14:53:20Z)
• One Token Embedding Is Enough to Deadlock Your Large Reasoning Model [91.48868589442837]
  We present the Deadlock Attack, a resource exhaustion method that hijacks an LRM's generative control flow.<n>Our method achieves a 100% attack success rate across four advanced LRMs.
  arXiv  Detail & Related papers  (2025-10-12T07:42:57Z)
• R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth? [63.51955244144878]
  R-HORIZON is a method designed to stimulate long-horizon reasoning behaviors in Large Reasoning Models (LRMs)<n>Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising complex multi-step reasoning tasks with interdependent problems that span long reasoning horizons.<n>Our analysis reveals that LRMs exhibit limited effective reasoning length and struggle to allocate thinking budget across multiple problems appropriately.
  arXiv  Detail & Related papers  (2025-10-09T13:16:22Z)
• BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit [12.189197763012409]
  Large language models (LRMs) have emerged as a significant advancement in artificial intelligence.<n>In this paper, we identify an unexplored attack vector against LRMs, which we term "overthinking tunables"<n>We propose a novel tunable backdoor, which moves beyond simple on/off attacks to one where an attacker can precisely control the extent of the model's reasoning verbosity.
  arXiv  Detail & Related papers  (2025-07-24T11:24:35Z)
• Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models [50.4652276723694]
  Think-RM generates flexible, self-guided reasoning traces that support advanced capabilities.<n>Think-RM achieves state-of-the-art results on RM-Bench, outperforming both BT RM and vertically scaled GenRM by 8%.
  arXiv  Detail & Related papers  (2025-05-22T05:56:11Z)
• SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning [14.020244011380063]
  SpecReason is a system that accelerates LRM inference.<n>It exploits the semantic flexibility of thinking tokens in preserving final-answer accuracy.<n>It achieves $1.4-3.0times$ speedup over vanilla LRM inference.
  arXiv  Detail & Related papers  (2025-04-10T16:05:19Z)
• ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation [38.64751082999587]
  Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely primarily on parametric knowledge, limiting factual accuracy.<n>We propose ReaRAG, a factuality-enhanced reasoning model that explores diverse queries without excessive iterations.<n>Our study enhances LRMs' factuality while effectively integrating robust reasoning for Retrieval-Augmented Generation (RAG)
  arXiv  Detail & Related papers  (2025-03-27T17:44:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.