[Paper] ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation

Source: arXiv - 2601.09703v1

Overview

The paper introduces ShortCoder, a framework that makes large‑language‑model (LLM) based code generation more token‑efficient without sacrificing correctness or readability. By automatically simplifying Python syntax before generation, ShortCoder cuts the number of tokens the model has to produce, which translates into faster inference and lower memory use—an important step toward practical, production‑grade AI coding assistants.

Key Contributions

• Syntax‑level simplification rules: Ten AST‑preserving transformations for Python that reduce code length by an average of 18.1 % while keeping behavior identical.
• ShorterCodeBench dataset: A large corpus of (original code, simplified code) pairs created through a hybrid pipeline that combines rule‑based rewriting with LLM‑guided polishing, ensuring semantic equivalence.
• Conciseness‑aware fine‑tuning: A training recipe that injects “shortness” knowledge into base LLMs, enabling them to prefer compact code during generation.
• Empirical validation: Demonstrated consistent token‑reduction (18.1 %–37.8 %) on the HumanEval benchmark with no drop in functional correctness, outperforming prior prompt‑compression or quantization tricks.

Methodology

1. Rule Design – The authors analyzed Python’s abstract syntax tree (AST) and crafted ten rewrite rules (e.g., replacing range(len(seq)) with enumerate(seq), collapsing multi‑line list comprehensions, removing redundant parentheses). Each rule is guaranteed to preserve the program’s semantics.
2. Data Synthesis – Starting from existing code corpora, they applied the rules to generate “shortened” versions. An LLM (e.g., GPT‑3.5) then refined these drafts to improve style and handle edge cases, producing the ShorterCodeBench pairs.
3. Fine‑tuning – The base code‑generation model is further trained on the (requirement → shortened code) pairs, with a loss that emphasizes token economy (e.g., adding a penalty for longer outputs).
4. Inference – At generation time, the model receives the user prompt as usual but is now biased to emit the compact syntax learned during fine‑tuning, eliminating the need for a separate post‑processing step.

Results & Findings

What it means: ShortCoder delivers almost identical functional performance while generating substantially fewer tokens, which directly cuts GPU memory footprints and speeds up the inference pipeline.

Practical Implications

• Faster AI pair‑programming tools – IDE plugins (e.g., GitHub Copilot, Tabnine) can integrate ShortCoder to reduce response times, especially on edge devices or low‑power servers.
• Cost savings – Cloud providers charge per token processed; a 20‑30 % token cut translates to noticeable monetary savings at scale.
• Better UX for mobile/embedded dev – Shorter outputs mean less scrolling and easier review for developers on constrained screens.
• Simplified downstream analysis – Compact code is easier for static analysis, linting, and security scanning tools, potentially improving the overall software supply chain.

Limitations & Future Work

• Language scope – The current rule set targets Python only; extending to JavaScript, Java, or Rust will require new AST‑preserving transformations.
• Edge‑case handling – Some aggressive rewrites may introduce subtle performance differences (e.g., using list comprehensions vs. explicit loops) that were not captured in the functional tests.
• Model dependence – The conciseness bias is learned during fine‑tuning; applying the same rules to a completely different LLM may need additional adaptation.
• Future directions – The authors suggest automating rule discovery via program synthesis, exploring multi‑objective fine‑tuning (balancing brevity, readability, and runtime efficiency), and evaluating on larger, real‑world codebases beyond benchmark suites.

Authors

• Sicong Liu
• Yanxian Huang
• Mingwei Liu
• Jiachi Chen
• Ensheng Shi
• Yuchi Ma
• Hongyu Zhang
• Yin Zhang
• Yanlin Wang

Paper Information

• arXiv ID: 2601.09703v1
• Categories: cs.SE, cs.AI, cs.CL
• Published: January 14, 2026
• PDF: Download PDF