@inproceedings{wen-etal-2026-coderise,
title = "{C}ode{R}ise: Bootstrapping {LLM}s for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum",
author = "Wen, Tengfei and
Chen, Xuanang and
He, Ben and
Cong, Xiaoliang and
Sun, Le",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1840/",
pages = "36929--36942",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) struggle with code generation for Ultra Low-Resource Programming Languages (ULRPLs) due to the scarcity of training data. Existing synthetic data generation methods fail in this context, suffering from a severe cold-start problem and resulting in samples that lack diversity. To overcome these challenges, we propose CodeRise, a novel two-stage framework that autonomously generates a high-quality, diverse, and progressively complex curriculum for ULRPLs. The framework first tackles the cold-start and distribution issues by leveraging the full formal syntax of the target language as structural guidance and applying a biased sampling strategy over library modules. Building on this foundation, we fine-tune the model to generate increasingly complex code without explicit syntax input, using an adaptive curriculum and multi-turn self-debugging to progressively improve code quality.We evaluate on two ULRPLs, Tengo and Janet, using migrated HumanEval-Tengo and MBPP-Tengo, as well as our new benchmarks, TengoEval and JanetEval. Experiments show that CodeRise significantly outperforms both training-free and training-based baselines in ultra low-resource environments."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wen-etal-2026-coderise">
<titleInfo>
<title>CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tengfei</namePart>
<namePart type="family">Wen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xuanang</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ben</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaoliang</namePart>
<namePart type="family">Cong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Le</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Large Language Models (LLMs) struggle with code generation for Ultra Low-Resource Programming Languages (ULRPLs) due to the scarcity of training data. Existing synthetic data generation methods fail in this context, suffering from a severe cold-start problem and resulting in samples that lack diversity. To overcome these challenges, we propose CodeRise, a novel two-stage framework that autonomously generates a high-quality, diverse, and progressively complex curriculum for ULRPLs. The framework first tackles the cold-start and distribution issues by leveraging the full formal syntax of the target language as structural guidance and applying a biased sampling strategy over library modules. Building on this foundation, we fine-tune the model to generate increasingly complex code without explicit syntax input, using an adaptive curriculum and multi-turn self-debugging to progressively improve code quality.We evaluate on two ULRPLs, Tengo and Janet, using migrated HumanEval-Tengo and MBPP-Tengo, as well as our new benchmarks, TengoEval and JanetEval. Experiments show that CodeRise significantly outperforms both training-free and training-based baselines in ultra low-resource environments.</abstract>
<identifier type="citekey">wen-etal-2026-coderise</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.1840/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>36929</start>
<end>36942</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum
%A Wen, Tengfei
%A Chen, Xuanang
%A He, Ben
%A Cong, Xiaoliang
%A Sun, Le
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wen-etal-2026-coderise
%X Large Language Models (LLMs) struggle with code generation for Ultra Low-Resource Programming Languages (ULRPLs) due to the scarcity of training data. Existing synthetic data generation methods fail in this context, suffering from a severe cold-start problem and resulting in samples that lack diversity. To overcome these challenges, we propose CodeRise, a novel two-stage framework that autonomously generates a high-quality, diverse, and progressively complex curriculum for ULRPLs. The framework first tackles the cold-start and distribution issues by leveraging the full formal syntax of the target language as structural guidance and applying a biased sampling strategy over library modules. Building on this foundation, we fine-tune the model to generate increasingly complex code without explicit syntax input, using an adaptive curriculum and multi-turn self-debugging to progressively improve code quality.We evaluate on two ULRPLs, Tengo and Janet, using migrated HumanEval-Tengo and MBPP-Tengo, as well as our new benchmarks, TengoEval and JanetEval. Experiments show that CodeRise significantly outperforms both training-free and training-based baselines in ultra low-resource environments.
%U https://aclanthology.org/2026.findings-acl.1840/
%P 36929-36942
Markdown (Informal)
[CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum](https://aclanthology.org/2026.findings-acl.1840/) (Wen et al., Findings 2026)
ACL