[Paper] Outcome-Conditioned Reasoning Distillation for Resolving Software Issues

Source: arXiv - 2601.23257v1

Overview

The paper introduces Outcome‑Conditioned Reasoning Distillation (O‑CRD), a new way to make large language model (LLM)–based bug‑fixing pipelines smarter by learning from already‑solved issues in the same codebase. Instead of starting from scratch for every new bug, O‑CRD walks backward from a verified patch, extracts the reasoning steps that led to it, and then re‑uses that distilled knowledge at inference time—without any extra fine‑tuning or expensive search.

Key Contributions

• Backward‑trace distillation: Reconstructs a step‑by‑step repair trace from a known good patch, turning the final outcome into a teaching signal.
• Outcome‑conditioned guidance: Provides a lightweight “reasoning hint” that steers both localization (which file/function to edit) and synthesis (what edit to apply) during inference.
• Zero‑fine‑tuning inference: The distilled guidance can be plugged into any LLM (GPT‑4o, DeepSeek‑V3, GPT‑5) without additional model updates or runtime search loops.
• Empirical boost on a realistic benchmark: On SWE‑Bench Lite, O‑CRD lifts Pass@1 by 10.4 % (GPT‑4o), 8.6 % (DeepSeek‑V3), and 10.3 % (GPT‑5) over strong baselines.
• Generalizable framework: Works across different LLM back‑ends, suggesting the approach is not tied to a specific model architecture.

Methodology

1. Collect historical fixes – For each resolved issue in a repository, the authors keep the final verified patch (the “outcome”).
2. Backward reconstruction – Starting from the outcome, they iteratively ask the LLM to explain how it could have arrived at that patch, generating a plausible chain of reasoning:
   • Identify the buggy location (file/function).
   • Enumerate constraints (e.g., test failures, type errors).
   • Propose incremental edits that gradually satisfy those constraints.
3. Distillation into a compact guide – The generated chain is compressed into a short “reasoning prompt” that captures the essential decision logic (e.g., “if the failure is a NullPointerException in X, first add a null‑check before using X”).
4. Inference with outcome‑conditioned guidance – When a new bug appears, the system retrieves the most similar historical guide (based on code similarity, error messages, etc.) and prepends it to the LLM’s prompt. The model then performs localization and patch synthesis once, guided by the distilled reasoning.
5. No online search – Unlike prior work that repeatedly refines patches or runs a search over many candidates, O‑CRD performs a single forward pass, dramatically cutting inference cost.

Results & Findings

• Higher success with a single attempt – The boost is measured at Pass@1, meaning the first generated patch is more often correct.
• Reduced latency – Because O‑CRD eliminates iterative refinement, average inference time drops by ~30 % compared with search‑based baselines.
• Robustness across models – Gains are consistent across three very different LLMs, indicating the distilled reasoning is model‑agnostic.

Practical Implications

• Faster CI/CD pipelines: Teams can integrate O‑CRD into automated pull‑request bots to get higher‑quality patches on the first try, shortening feedback loops.
• Lower cloud costs: Eliminating multi‑step search reduces token usage, which translates directly into cheaper API bills for organizations that rely on commercial LLMs.
• Knowledge reuse within monorepos: Large codebases (e.g., Google, Meta) often contain recurring bug patterns; O‑CRD automatically harvests and re‑applies that institutional knowledge without manual rule engineering.
• Developer assistance tools: IDE extensions can surface the distilled reasoning as “suggested debugging steps,” giving developers a transparent view of why a particular edit is recommended.
• Cross‑project portability: Since the guide is a lightweight textual prompt, it can be exported and shared across projects or even open‑source repositories, fostering community‑wide repair heuristics.

Limitations & Future Work

• Quality of backward traces: The reconstruction relies on the LLM’s ability to generate plausible reasoning from a final patch; noisy traces could mislead the guide.
• Similarity matching: Selecting the most relevant historical guide is currently based on simple code‑similarity heuristics; more sophisticated retrieval (e.g., graph‑based or semantic embeddings) could improve relevance.
• Scope of bugs: The evaluation focuses on SWE‑Bench Lite, which emphasizes typical open‑source bugs; performance on highly domain‑specific or security‑critical defects remains untested.
• Extending beyond patches: Future work could explore distilling reasoning for larger refactorings, performance optimizations, or even design‑level decisions.

Overall, O‑CRD demonstrates that “learning from the outcome” can be a cheap yet powerful alternative to costly forward search, opening a new avenue for practical, LLM‑driven software maintenance.

Authors

• Chenglin Li
• Yisen Xu
• Zehao Wang
• Shin Hwei Tan
• Tse‑Hsun
• Chen

Paper Information

• arXiv ID: 2601.23257v1
• Categories: cs.SE
• Published: January 30, 2026
• PDF: Download PDF