Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework (2509.14093v1)

Abstract: Chain-of-Thought (CoT) reasoning enhances LLMs by prompting intermediate steps, improving accuracy and robustness in arithmetic, logic, and commonsense tasks. However, this benefit comes with high computational costs: longer outputs increase latency, memory usage, and KV-cache demands. These issues are especially critical in software engineering tasks where concise and deterministic outputs are required. To investigate these trade-offs, we conduct an empirical study based on code generation benchmarks. The results reveal that longer CoT does not always help. Excessive reasoning often causes truncation, accuracy drops, and latency up to five times higher, with failed outputs consistently longer than successful ones. These findings challenge the assumption that longer reasoning is inherently better and highlight the need for adaptive CoT control. Motivated by this, we propose SEER (Self-Enhancing Efficient Reasoning), an adaptive framework that compresses CoT while preserving accuracy. SEER combines Best-of-N sampling with task-aware adaptive filtering, dynamically adjusting thresholds based on pre-inference outputs to reduce verbosity and computational overhead. We then evaluate SEER on three software engineering tasks and one math task. On average, SEER shortens CoT by 42.1%, improves accuracy by reducing truncation, and eliminates most infinite loops. These results demonstrate SEER as a practical method to make CoT-enhanced LLMs more efficient and robust, even under resource constraints.

• The paper demonstrates that adaptive chain-of-thought compression via SEER significantly reduces reasoning lengths and improves performance.
• It introduces a three-stage methodology—pre-inference generation, Best-of-N sampling, and adaptive filtering—to balance conciseness and correctness.
• Experimental results show a 42.1% reduction in CoT length, decreased latency, and enhanced robustness across diverse datasets.

Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework

Introduction

This paper addresses the computational inefficiency and instability introduced by verbose Chain-of-Thought (CoT) reasoning in LLMs, particularly in software engineering tasks. While CoT prompting enhances model interpretability and accuracy by eliciting explicit intermediate reasoning steps, it also incurs substantial inference latency, memory overhead, and increased risk of output truncation and infinite reasoning loops. The authors empirically demonstrate that longer CoT does not guarantee improved performance; in fact, excessive reasoning often correlates with reduced accuracy and higher truncation rates. To address these challenges, the paper introduces SEER (Self-Enhancing Efficient Reasoning), a framework for adaptive CoT compression that autonomously refines reasoning traces to balance conciseness and correctness, without reliance on external compression modules.

Empirical Analysis of CoT Length and Model Performance

The authors conduct a comprehensive empirical paper using DeepSeek-R1-Distill-Qwen models (1.5B, 7B, 14B, 32B) on HumanEval and Codeforces datasets. The analysis reveals several key findings:

• Truncation and Reasoning Loops: Excessively long CoT outputs frequently lead to truncation, especially in smaller models, resulting in significant accuracy degradation. Reasoning loops—repetitive output fragments—are the dominant cause of truncation, wasting context and computational resources.
• CoT Length vs. Accuracy: Contrary to prior assumptions, longer CoT chains are associated with failed outputs, while successful generations tend to have shorter reasoning traces.

  Figure 1: Distribution of CoT lengths for passed and failed cases across DeepSeek-R1-Distill-Qwen models (7B, 14B, and 32B) on HumanEval/129. Successful outputs do not require longer CoT; excessive length correlates with failure.

• Latency Overhead: CoT reasoning substantially increases inference latency, with larger models and more complex tasks (e.g., Codeforces) experiencing up to 5x slower response times.

  Figure 2: Inference latency of different model sizes on HumanEval and Codeforces datasets with and without CoT reasoning. CoT increases latency, especially for larger models and complex tasks.

These findings motivate the need for adaptive control over CoT length to preserve efficiency and reliability in real-world deployments.

SEER Framework: Architecture and Implementation

SEER is designed as a self-enhancing, autonomous framework for CoT compression, comprising three main stages:

1. Pre-Inference Generation: The base model generates multiple CoT-augmented candidate responses for each input, using either dataset-specific or general-purpose prompts. Sufficient token budgets are allocated to capture the natural distribution of reasoning lengths.
2. Best-of-N (BoN) Sampling: For each input, N candidate responses are generated. Candidates are filtered hierarchically: (a) correctness of the final answer, (b) presence of a valid CoT, and (c) minimal CoT length. The shortest correct reasoning trace is selected, suppressing reasoning loops and promoting conciseness.
3. Adaptive CoT Filtering: A dataset-specific maximum CoT length $\lambda_c$ is computed as the mean-median average of observed CoT lengths. Responses exceeding $\lambda_c$ are discarded, balancing compression against risk of information loss.

   Figure 3: Overview of the SEER framework, illustrating the self-enhancing procedure of generation, selection, and adaptive filtering.

The final fine-tuning stage uses the curated, concise dataset to update the model parameters, internalizing efficient reasoning patterns.

Experimental Results

CoT Compression and Accuracy

SEER is evaluated on MathQA-Python, CodeXGLUE-Defect-Detection, Code-Search-WebQuery, and GSM-8K. Compared to baselines (Self-Training, TokenSkip, Naive BoN), SEER achieves:

• Average CoT length reduction of 42.1% across tasks.
• No loss in accuracy; in some cases, accuracy improves due to reduced truncation and loop suppression.
• Superior robustness in software engineering tasks, where other compression methods (e.g., TokenSkip) often corrupt code logic or fail to execute.

Ablation Studies

Ablation experiments confirm that:

• Adaptive filtering is the primary driver of efficiency and accuracy.
• BoN sampling provides complementary benefits, enhancing robustness and further reducing reasoning loops.
• Overly aggressive filtering can harm accuracy, but the mean-median adaptive threshold achieves optimal trade-off.

Cross-Domain Generalization

SEER fine-tuned on one domain (e.g., code generation) generalizes well to other domains (e.g., mathematical reasoning), maintaining concise reasoning and competitive accuracy without retraining.

Figure 4: Performance of SEER fine-tuned on different datasets when evaluated on HumanEval. Coding-related datasets yield best generalization with shorter CoT lengths; non-coding datasets still provide moderate improvements.

Loop Mitigation

SEER suppresses infinite reasoning loops by up to 97.7%, substantially reducing truncation rates and improving output stability.

Implementation Considerations

• Computational Requirements: SEER is compatible with both full-parameter SFT and parameter-efficient fine-tuning (e.g., LoRA), enabling deployment in resource-constrained environments.
• Prompt Agnosticism: The framework is robust to prompt format, as adaptive filtering and BoN sampling mitigate prompt sensitivity.
• Scalability: SEER can be integrated into existing LLM training pipelines with minimal modification, as it does not require external compression modules or complex training objectives.
• Limitations: Evaluation is primarily on DeepSeek-R1-Distill-Qwen; future work should extend to other architectures and reasoning domains.

Theoretical and Practical Implications

The results challenge the prevailing assumption that longer CoT chains are always beneficial. Instead, they demonstrate that concise, well-controlled reasoning traces yield higher accuracy, lower latency, and improved robustness. SEER's adaptive compression mechanism provides a principled approach to balancing reasoning sufficiency and computational efficiency, with strong transferability across domains.

Practically, SEER enables CoT-enhanced LLMs to operate effectively in latency-sensitive and resource-constrained settings, such as real-time code generation, automated debugging, and mathematical problem solving. The framework's simplicity and autonomy facilitate integration into production systems without reliance on external annotation or compression tools.

Future Directions

Potential avenues for future research include:

• Extending SEER to implicit CoT models and closed-source architectures.
• Systematic evaluation across additional reasoning domains (e.g., commonsense, legal, scientific).
• Stratified analysis by task complexity to refine adaptive filtering strategies.
• Integration into latency-critical applications and real-world developer workflows.

Conclusion

This work provides a rigorous analysis of the inefficiency and instability introduced by verbose CoT reasoning in LLMs, particularly in software engineering contexts. The SEER framework offers an effective, autonomous solution for adaptive CoT compression, achieving substantial reductions in reasoning length while preserving or improving accuracy and robustness. Theoretical and empirical results underscore the importance of controlled reasoning length, challenging the notion that "more thinking" is always better. SEER represents a practical advancement for efficient, reliable deployment of CoT-enhanced LLMs in diverse real-world applications.