SwiReasoning: Entropy-Pushed Alternation of Latent and Express Chain-of-Thought for Reasoning LLMs

SwiReasoning is a decoding-time framework that lets a reasoning LLM determine when to suppose in latent area and when to write specific chain-of-thought, utilizing block-wise confidence estimated from entropy tendencies in next-token distributions. The tactic is training-free, model-agnostic, and targets Pareto-superior accuracy/effectivity trade-offs on arithmetic and STEM benchmarks. Reported outcomes present +1.5%–2.8% common accuracy enhancements with limitless tokens and +56%–79% common token-efficiency positive factors below constrained budgets; on AIME’24/’25, it reaches most reasoning accuracy earlier than commonplace CoT.

What SwiReasoning modifications at inference time?

The controller screens the decoder’s next-token entropy to type a block-wise confidence sign. When confidence is low (entropy trending upward), it enters latent reasoning—the mannequin continues to purpose with out emitting tokens. When confidence recovers (entropy trending down), it switches again to specific reasoning, emitting CoT tokens to consolidate and decide to a single path. A swap depend management limits the utmost variety of thinking-block transitions to suppress overthinking earlier than finalizing the reply. This dynamic alternation is the core mechanism behind the reported accuracy-per-token positive factors.

Outcomes: accuracy and effectivity on commonplace suites

It stories enhancements throughout arithmetic and STEM reasoning duties:

• Move@1 (limitless price range): accuracy lifts as much as +2.8% (math) and +2.0% (STEM) in Determine 1 and Desk 1, with a +2.17% common over baselines (CoT with sampling, CoT grasping, and Smooth Pondering).
• Token effectivity (restricted budgets): common enhancements as much as +79% (Determine 2). A complete comparability reveals SwiReasoning attains the highest token effectivity in 13/15 evaluations, with an +84% common enchancment over CoT throughout these settings (Determine 4).
• Move@okay dynamics: with Qwen3-8B on AIME 2024/2025, most reasoning accuracies are achieved +50% earlier than CoT on common (Determine 5), indicating quicker convergence to the ceiling with fewer sampled trajectories.

Why switching helps?

Express CoT is discrete and readable however locks in a single path prematurely, which might discard helpful options. Latent reasoning is steady and information-dense per step, however purely latent methods could diffuse chance mass and impede convergence. SwiReasoning provides a confidence-guided alternation: latent phases broaden exploration when the mannequin is unsure; specific phases exploit rising confidence to solidify an answer and commit tokens solely when helpful. The swap depend management regularizes the method by capping oscillations and limiting extended “silent” wandering—addressing each accuracy loss from diffusion and token waste from overthinking cited as challenges for training-free latent strategies.

Positioning vs. baselines

The challenge compares towards CoT with sampling, CoT grasping, and Smooth Pondering, reporting a +2.17% common accuracy carry at limitless budgets (Desk 1) and constant efficiency-per-token benefits below price range constraints. The visualized Pareto frontier shifts outward—both larger accuracy on the identical price range or related accuracy with fewer tokens—throughout totally different mannequin households and scales. On AIME’24/’25, the Move@okay curves present that SwiReasoning reaches the efficiency ceiling with fewer samples than CoT, reflecting improved convergence conduct moderately than solely higher uncooked ceilings.

Key Takeaways

• Coaching-free controller: SwiReasoning alternates between latent reasoning and specific chain-of-thought utilizing block-wise confidence from next-token entropy tendencies.
• Effectivity positive factors: Stories +56–79% common token-efficiency enhancements below constrained budgets versus CoT, with bigger positive factors as budgets tighten.
• Accuracy lifts: Achieves +1.5–2.8% common Move@1 enhancements on arithmetic/STEM benchmarks at limitless budgets.
• Sooner convergence: On AIME 2024/2025, reaches most reasoning accuracy earlier than CoT (improved Move@okay dynamics).

SwiReasoning is a helpful step towards pragmatic “reasoning coverage” management at decode time: it’s training-free, slots behind the tokenizer, and exposes measurable positive factors on math/STEM suites by toggling between latent and specific CoT utilizing an entropy-trend confidence sign with a capped swap depend. The open-source BSD implementation and clear flags (--max_switch_count, --alpha) make replication simple and decrease the barrier to stacking with orthogonal effectivity layers (e.g., quantization, speculative decoding, KV-cache tips). The tactic’s worth proposition is “accuracy per token” moderately than uncooked SOTA accuracy, which is operationally necessary for budgeted inference and batching.

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