SynthCoder: AI for Code Completion & Creative Synthesis

• SynthCoder is a framework combining program synthesis and creative audio generation, leveraging AST-based data extraction and heuristic FIM sampling for realistic code completion.
• It utilizes multi-dimensional context enrichment like BM25 retrieval and call graph construction to integrate local and repository-level code dependencies.
• The two-stage training pipeline with curriculum learning and Direct Preference Optimization refines model alignment and effectively minimizes repetitive code output.

SynthCoder refers to a series of distinct systems and research approaches across program synthesis, code completion, and creative sound generation. In the field of software engineering and artificial intelligence, the most recent and prominent usage denotes an LLM-tuning strategy for code completion (Yu et al., 21 Aug 2025), as well as prior works addressing API-centric code synthesis (Nam et al., 2022) and example-driven synthesis in functional languages (Mulleners et al., 2022). SynthCoder also appears as the name or analog of creative systems for virtual audio synthesis from text (Cherep et al., 1 Jun 2024) and quantum simulation (Freye et al., 1 Feb 2024). This article provides a comprehensive and technically detailed treatment of SynthCoder, focusing primarily on its role in large-scale language-model tuning for code completion, with contextual connections to related neural synthesis and creative audio systems.

1. Dataset Construction for Code Completion

Contemporary forms of SynthCoder for code completion adopt a synthetic, structurally-diverse dataset generation strategy that simulates real-world developer behaviors. The pipeline parses code bases from multiple programming languages using parsers such as Tree-sitter, converting the code into Abstract Syntax Trees (AST). The system identifies fine-grained AST nodes—classes, methods, assignments, comments, conditionals—designated as natural “units” or anchor points for code completion training. Beyond structural extraction, heuristic mechanisms are introduced to emulate typical developer patterns observed during editing, such as partial statements or comment-driven anticipation of subsequent code. These heuristics add realistic Fill-in-the-Middle (FIM) training samples, capturing complex insertion contexts that frequently arise in practical scenarios, rather than limiting the training data to file-level prefix completions.

By integrating both AST-derived fragments and heuristics that simulate actual developer activity, the constructed dataset enhances the realism and diversity of completion prompts, directly supporting the FIM paradigm. Structural and behavioral diversity in training samples is crucial to generalization across unseen editing situations in real-world codebases.

2. Training Corpus Enrichment via Cross-File Context Retrieval

To enable repository-level code completion, SynthCoder applies multi-dimensional context enrichment strategies on its pre-training corpus. The two principal modes are:

• BM25 Similarity Retrieval: For each completion prompt, similar code snippets are retrieved across the repository using the BM25 ranking algorithm. Code files are segmented into short text chunks (typically ≤20 lines). Cross-file retrieval introduces analogy and examples from semantically-related code, allowing the model to recognize patterns and solutions that transcend single-file boundaries.
• Call Graph Construction: Code dependencies are modeled with call graphs that represent function call and import relationships throughout the repository. These graphs are leveraged to incorporate intra-repository functional context, so the model is exposed to usage patterns, dependencies, and definitions that may be separated across files.

This dual-pronged enrichment significantly augments the information available to the model at both the local (file) and global (repository) levels, directly improving the model’s ability to generate context-aware and semantically consistent completions in large codebases.

3. Two-Stage Training and Alignment Pipeline

SynthCoder is trained in two distinct phases for maximal performance on code completion:

A. Curriculum-Based Fine-Tuning

• Training data is first parsed via Tree-sitter to obtain ASTs, and each code fragment is assigned a quantitative complexity measure (e.g., number of identifier nodes).
• Fragments are sorted in descending order of complexity; only the most complex k% (default: 30%) are selected for training, biasing the model toward samples reflective of challenging real-world scenarios.
• Curriculum learning proceeds from simple to complex examples, promoting stable convergence and better adaptation to nuanced code patterns.
• The optimizer uses AdamW with learning rates such as 3e-6, and training involves step-wise warmup schedules to avoid destabilization.

B. Direct Preference Optimization (DPO) with Repetition Suppression

• Post fine-tuning, model alignment is achieved via Direct Preference Optimization (DPO). Here, for each FIM sample, multiple candidate completions are generated via rejection sampling.
• Preference pairs are constructed—one preferred (human-like, contextually relevant) and one dispreferred (e.g., repetitive or verbatim from context).
• DPO’s loss is:

$$\mathcal{L}(\pi_\theta;\pi_\text{ref}) = -\mathbb{E}_{(x, y_w, y_l)\sim \mathcal{D}} \left[ \log \sigma\left(\beta\left( \log \frac{\pi_\theta(y_w|x)}{\pi_{\text{ref}}(y_w|x)} - \log \frac{\pi_\theta(y_l|x)}{\pi_{\text{ref}}(y_l|x)} \right)\right)\right]$$

where $x$ is the prompt, $y_w$/$y_l$ are preferred/dispreferred completions, $\pi_\theta$ is the aligned model, $\pi_\text{ref}$ the base model, and $\beta$ a scaling factor (0.9).

• Repetition-punishing preference pairs are explicitly incorporated, directly discouraging the model from repeating preceding or succeeding code (prefix or suffix).

This two-stage process results in an LLM that not only generates syntactically valid completions but also aligns well with subjective developer preferences and avoids pathological repetition.

4. Performance Evaluation and Benchmarking

SynthCoder is evaluated using diverse completion metrics on multiple standardized datasets:

Benchmark	Metrics	SynthCoder Outcome
aiXcoder	EM, ES	Substantial improvement vs. baseline
ExecRepoBench	Pass@1, ES	+24% Pass@1, +35% ES over base model
CrossCodeEval	EM, ES, ES_repo	Robust multi-language performance
CoLT	EM, ES	Gains of ~7% in EM/ES across languages

Key metrics:

• Exact Match (EM): Percentage of completions identical to ground truth region.
• Edit Similarity (ES): String similarity (edit distance) between completion and target.
• Pass@1: Fraction of cases where the first suggestion is correct/executable.

SynthCoder’s architectural and data-centric innovations deliver consistent, significant gains across these evaluation axes, particularly at the repository (multi-file) level.

5. Minimization of Repetitive Code Output

Mitigation of undesired code repetition—a frequent problem in generic LLM-based completion—receives dedicated treatment in SynthCoder. During dataset curation, negative completions that precisely repeat surrounding code are generated and tagged as dispreferred. These samples enter the DPO alignment phase as negative examples, instructing the model to penalize outputs that merely repeat prefix or suffix. Such explicit repetition suppression reduces the probability mass assigned to degenerate or redundant completions, avoiding wasted inference and model stalling typical in many existing approaches.

This targeted approach leverages preference supervision, resulting in marked decreases in code repetition on practical benchmarks and improved developer experience in deployment.

6. Related Approaches and Research Context

Several related threads in neural code and program synthesis intersect with the SynthCoder paradigm:

• Latent Execution and Partial Program Synthesis: LaSynth (Chen et al., 2021) advances neural synthesis for C-like languages via dual modules—a program decoder and a latent executor that “imagines” the execution trace of even sensorily incomplete programs. The approach directly improves next-token prediction and generalization, achieving ~55.2% correctness in restricted C synthesis.
• API-Centric Sequential Synthesis: SynthCoder (Nam et al., 2022) in an alternative sense refers to a deep learning model that predicts API call sequences to map input tensors to outputs in libraries like Numpy or TensorFlow. The architecture employs tensor semantic encoding and recurrent composition, achieving a top-1 accuracy of 79% on real benchmarks and reducing synthesis times from 10.01s (plain search) to 1.04s (Full-Sequence model).
• Constraint-Based Functional Synthesis: Scrybe (“SynthCoder” in (Mulleners et al., 2022)) fuses top-down deductive synthesis with live bidirectional evaluation, propagating example constraints through program sketches, yielding median synthesis runtimes of ~16ms and >5× speed-ups.
• Creative Audio and Sound Synthesis: SynthCoder analogues in creative sound systems include text-to-audio generation using interpretable synthesizer parameters (Cherep et al., 1 Jun 2024) and quantum-audio synthesis via numeric simulation of the Schrödinger equation (Freye et al., 1 Feb 2024).

A plausible implication is that the field is converging on context-sensitive, interpretability-driven, and cross-modal paradigms for program and content synthesis.

7. Implications, Limitations, and Future Directions

SynthCoder exemplifies a trend toward highly engineered training regimens leveraging structural code analysis (ASTs, call graphs), cross-file retrieval, curriculum learning, and explicit human-alignment objectives—all tailored for production-quality code completion. Its improvements in FIM and repository-level tasks overcome the “seesaw” trade-offs observed in prior art, wherein optimizing for one metric (e.g., accuracy) degraded others (e.g., code repetition).

Remaining limitations include the dependence on accurate AST extraction/decomposition, potential brittleness to heuristic errors in dataset construction, and scalability considerations for extremely large, heterogeneous codebases. Future research may assess the extensibility of SynthCoder’s alignment strategies to even larger models and integrate more advanced preference alignment mechanisms (potentially instruction-based or adversarially generated negatives).

Conclusion

SynthCoder, as presented in (Yu et al., 21 Aug 2025), integrates AST-based data synthesis, cross-file corpus enrichment, curriculum learning, and Direct Preference Optimization to achieve state-of-the-art code completion performance. The approach is validated on leading benchmarks including aiXcoder, ExecRepoBench, CrossCodeEval, and CoLT, and improves model alignment with developer intent while suppressing code repetition. SynthCoder’s methodology situates it as a template for robust, context-aware LLM tuning in code intelligence, with conceptual resonance across neural program synthesis and creative generative systems.