SelfCodeAlign: Self-Alignment for Code Generation

Authors: yuxiang  wei
federico  cassano
jiawei  liu
yifeng  ding
naman  jain
zachary  mueller
harm  de vries
leandro  von werra
arjun  guha
lingming  zhang
l  maaten
l  chen
l  tan
l  jenkins
l  martin
l  madaan
l  malo
l  blecher
l  landzaat
m  de oliveira
m  muzzi
m  pasupuleti
m  singh
m  paluri
m  kardas
m  oldham
m  rita
m  pavlova
m  kambadur
m  lewis
m k si
n  hassan
n  goyal
n  torabi
n  bashlykov
n  bogoychev
o  chatterji
o  duchenne
p  çelebi
p  alrassy
p  zhang
p  li
p  vasic
p  weng
p  bhargava
p  dubal
p s krishnan
p  koura
q  xu
q  he
r  dong
r  srinivasan
r  ganapathy
r s calderer
r  cabral
r  stojnic
r  raileanu
r  gird- har
r  patel
r  sauvestre
r  polidoro
r  sumbaly
r  taylor
r  silva
  hou
t  borodinsky
t  herman
t  fowler
t  sheasha
t  geor- giou
t  scialom
t  speckbacher
t  mihaylov
u  xiao
v  karn
v  goswami
v  gupta
v  ramanathan
v  kerkez
v  gonguet
v  do
v  vogeti
w  petrovic
w  chu
w  xiong
w  fu
x  meers
x  martinet
x e wang
x  tan
x  xie
x  jia
yu  wang
y  goldschlag
y  gaur
y  babaei
y  wen
y  song
y  zhang
y  li
z d mao
z  coudert
z  yan
z  chen
a  papakipos
a  singh
a  grattafiori
a  jain
a  kelsey
a  shajnfeld
a  gangidi
a  victoria
a  goldstand
a  menon
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a  sangani
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a  gu
a  ho
a  poulton
a  ryan
a  ramchandani
a  franco
a  saraf
a  chowdhury
a  gabriel
a  bharambe
a  eisenman
b  yazdan
b  james
b  maurer
b  leonhardi
b  huang
b d loyd
b  paola
b  paranjape
b  liu
b  wu
b  ni
b  hancock
b  wasti
b  spence
b  stojkovic
b  gamido
c  montalvo
c  parker
c  burton
c  mejia
c  wang
c  kim
c  zhou
c.-h  hu
c  chu
c  cai
c  tindal
d  feichtenhofer
d  civin
d  beaty
d  kreymer
d  li
d  wyatt
d  adkins
d  xu
d  testuggine
d  david
d  parikh
d  liskovich
d  foss
d  wang
d  le
e  holland
e  dowling
e  jamil
e  montgomery
e  presani
e  hahn
e  wood
e  brinkman
e  ar- caute
e  dunbar
f  smothers
f  sun
f  kreuk
f  tian
f  ozgenel
f  caggioni
f  guzmán
f  kanayet
g m seide
g  florez
g  schwarz
g  badeer
g  swee
g  halpern
g  that- tai
g  herman
  sizov
  guangyi
g  zhang
h  lakshminarayanan
h  shojanazeri
h  zou
h  wang
h  zha
h  habeeb
h  rudolph
h  suk
h  aspegren
i  goldman
  damlaj
k  wang
k h wu
k  saxena
k  prasad
k  khandelwal
k  zand
k  matosich
k  veeraraghavan
k  michelena
k  li
k  huang
k  chawla
k  lakhotia
l  huang
l a garg
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l  bell
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l  yu
l  moshkovich
l  wehrstedt
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m  reso
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m  keneally
m l seltzer
m  valko
m  restrepo
m  patel
m  vyatskov
m  samvelyan
m  clark
m  macey
m  wang
m j hermoso
m  metanat
m  rastegari
m  bansal
n  santhanam
n  parks
n  white
n  bawa
n  singhal
n  egebo
n  usunier
n p laptev
n  dong
n  zhang
n  cheng
o  chernoguz
o  hart
o  salpekar
o  kalinli
p  kent
p  parekh
p  saab
p  balaji
p  rittner
p  bontrager
p  roux
p  dollar
p  zvyagina
p  ratanchandani
p  yuvraj
q  liang
r  alao
r  rodriguez
r  ayub
r  murthy
r  nayani
r  mitra
r  li
r  hogan
r  battey
r  wang
r  maheswari
r  howes
t  remez
t  glaser
t  best
t  kohler
t  robinson
t  li
t  zhang
t  matthews
t  chou
t  shaked
v  vontimitta
v  ajayi
v  montanez
v  mohan
v s kumar
v  mangla
v  albiero
v  ionescu
v  poenaru
v t mihailescu
v  ivanov
w  li
w  wang
w  jiang
w  bouaziz
w  constable
x  tang
x  wu
x  xia
x  gao
y  chen
y  hu
y  jia
y  qi
y  adi
y  nam
y  hao
y  qian
y  he
z  rait
z  devito
z  rosnbrick
z  wen
z  yang
z  zhao
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-13 16:44:03

Abstract: Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. For programming tasks, most models are finetuned with costly human-annotated instruction-response pairs or those generated by large, proprietary LLMs, which may not be permitted. We propose SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs without extensive human annotations or distillation. SelfCodeAlign employs the same base model for inference throughout the data generation process. It first extracts diverse coding concepts from high-quality seed snippets to generate new tasks. It then samples multiple responses per task, pairs each with test cases, and validates them in a sandbox environment. Finally, passing examples are selected for instruction tuning. In our primary experiments, we use SelfCodeAlign with CodeQwen1.5-7B to generate a dataset of 74k instruction-response pairs. Finetuning on this dataset leads to a model that achieves a 67.1 pass@1 on HumanEval+, surpassing CodeLlama-70B-Instruct despite being ten times smaller. Across all benchmarks, this finetuned model consistently outperforms the original version trained with OctoPack, the previous state-of-the-art method for instruction tuning without human annotations or distillation. Additionally, we show that SelfCodeAlign is effective across LLMs of various sizes, from 3B to 33B, and that the base models can benefit more from alignment with their own data distribution. We further validate each component’s effectiveness in our pipeline, showing that SelfCodeAlign outperforms both direct distillation from GPT-4o and leading GPT-3.5-based distillation methods, such as OSS-Instruct and Evol-Instruct. SelfCodeAlign has also led to the creation of StarCoder2-Instruct, the first fully transparent, permissively licensed, and self-aligned code LLM that achieves state-of-the-art coding performance. Overall, SelfCodeAlign shows for the first time that a strong instruction-tuned code LLM can result from self-alignment rather than distillation.

Main paper: 
 introduction: 
 Recent studies have demonstrated the outstanding performance of large language models (LLMs) [33, 40, 19, 57, 45, 69, 8, 70] in various code-related tasks, e.g., program synthesis [8, 3] , program repair [78, 27, 24, 79, 73] , code optimization [59, 9] , code completion [11, 40, 19] , code translation [56, 1, 51] , software testing [32, 10, 42, 77] , and software agents [80, 67, 75, 37] . The reason is that modern LLMs are pre-trained over trillions of code tokens in the wild using various training objectives (as such next-token prediction [52] ), making the base models natively good at understanding and generating code snippets. Furthermore, to fully unleash the power of LLMs, the base models are typically further fine-tuned on high-quality instruction-following data to boost their performance in following natural language instructions and solving more general software engineering tasks [25] . This step is known as instruction tuning [50] .Curating high-quality data for instruction tuning is crucial yet challenging. One source of acquiring instruction data is to employ human annotation [50] . For example, Llama-3 [14] uses a corpus of 10 million human-annotated examples in instruction tuning. Due to the high cost of human annotation, knowledge distillation is widely adopted to train a weaker LLM with outputs generated by stronger LLMs [18] . However, distillation may violate the terms of service [48, 17, 2] of proprietary LLMs and the prerequisite of using a stronger LLM limits its generalizability. Therefore, recent proposals focus on instruction tuning without relying on human annotation or distillation [34, 60, 82] . One cornerstone work along this direction is SELF-INSTRUCT [68] , which finetunes GPT-3 with self-generated instruction data using in-context learning.There is a growing number of instruction-tuned open-source LLMs in the code domain. However, some models, such as DeepSeek-Coder [19] , Llama-3 [14] , and CodeQwen1.5 [64] , either use proprietary data or do not disclose their instruction-tuning strategies. Others, including WizardCoder [41] , Magicoder [72] , WaveCoder [81] , and OpenCodeInterpreter [83] , rely on knowledge distillation. The only exception is OctoCoder [43] , which is instruction-tuned over heavily filtered GitHub commits, with commit messages as instructions and the changed code as responses, as well as data from Ope-nAssistant, a human-generated corpus of user-assistant conversations [29] . Despite its transparency and permissive licensing, OctoCoder's performance, at 32.9 HumanEval+ pass@1, lags behind other mainstream code LLMs. Meanwhile, previous attempts at applying SELF-INSTRUCT for code generation have resulted in performance degradation over training on natural instruction-response pairs [43] . Our findings imply that effective self-alignment requires a combination of data diversity control and response validation, which is not present in the traditional SELF-INSTRUCT approach.In this paper, we propose SelfCodeAlign, the first fully transparent pipeline to successfully self-align base code LLMs with purely self-generated instruction data. First, SelfCodeAlign extracts diverse coding concepts from high-quality seed functions in The Stack V1 [28] , a large corpus of permissively licensed code. Next, using these concepts, we prompt the base model to generate new coding tasks through in-context learning. We then instruct the base model to produce multiple responses for each task, each paired with test cases for self-validation. Finally, we select only the instruction-response pairs that pass the test cases. This method ensures the model practices various coding concepts and validates the consistency between instructions and responses.To evaluate our method, we train CodeQwen1.5-7B, a state-of-the-art open-source base LLM for code, on both a dataset generated with SelfCodeAlign and OctoPack, a naturally-generated and meticulously-filtered dataset used for training OctoCoder [43] . We benchmark both, along with OctoCoder and other models, on a series of tasks: code generation (both function-and classlevel) [38, 21, 76, 13] , data science programming [30] , and code editing [6] . On all tasks, training CodeQwen with SelfCodeAlign significantly improves performance over the base model and over training it on OctoPack. For instance, on HumanEval+, our model achieves a pass@1 score of 67.1, 21.4 points higher than CodeQwen1.5-7B and 16.5 points higher than CodeQwen1.5-7B-OctoPack. This highlights the effectiveness of our synthetic data generation method compared to natural data in enhancing the capabilities of code LLMs.In the component analysis, we justify the different components of the pipeline. We demonstrate that SelfCodeAlign is general to different LLMs whose sizes range from 3B to 33B. In particular, we find that a base LLM could learn more effectively from data within its own distribution than a shifted distribution from a teacher LLM. Additionally, we show that seed selection, concept generation, and execution filtering all contribute positively to the pipeline. Furthermore, on HumanEval+, Self-CodeAlign (67.1 pass@1) outperforms state-of-the-art, GPT-3.5-Turbo-based distillation methods, including OSS-Instruct [72] (61.6) and Evol-Instruct [65] (59.1), as well as direct output distillation from GPT-4o [49] (65.9) .SelfCodeAlign has also led to the creation of StarCoder2-Instruct, the first fully transparent, permissively licensed, and self-aligned code LLM that achieves state-of-the-art coding performance. We discuss StarCoder2-Instruct in Appendix A.Overall, we make the following main contributions: (i) We introduce SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs to follow instructions. Our method does not rely on extensive human annotations or distillation from larger models. (ii) We generate a series of datasets using SelfCodeAlign and train multiple models on these datasets, which will all be released to the public. (iii) We thoroughly evaluate our method on a multitude of tasks, showing strong performance across all the evaluated models. (iv) Our experiments demonstrate that training models on their own data can be more effective than using data from stronger, but distributionally different, teacher models when they don't have a huge performance gap. (v) Finally, we run a comprehensive component analysis that verifies the positive contribution of each component in SelfCodeAlign.2 SelfCodeAlign: Self-Alignment for Code Generation Figure 1 illustrates an overview of our SelfCodeAlign technique. It first generates diverse instructions by extracting coding concepts from high-quality seed snippets. This process resembles OSS-Instruct [72] , which employs GPT-3.5-Turbo to convert random snippets into instructions. However, our method uses the base model exclusively and incorporates a separate concept generation phase that we prove beneficial in §4. 3 . SelfCodeAlign then generates several responses for each task, pairing each with test cases for sandbox execution, and finally chooses passing examples for instruction tuning. Example outputs from each step are listed in Appendix D.1. In the following sections, we provide detailed explanations of these steps.  
seed snippets collection: 
 SelfCodeAlign starts by collecting a set of seed code snippets from The Stack V1. In this step, it's crucial to ensure that the seed snippets are diverse and high-quality, as they will be used as the starting point for generating instructions and responses. To collect the seed snippets, we extract all Python functions with docstrings from The Stack V1, and then apply a series of filtering rules to ensure the quality of the seed snippets. In total, we collect 250k Python functions from 5M functions with docstrings in The Stack V1, which were filtered by running the Pyright type checker, removing benchmark items, filtering out functions with poor documentation, and removing near-duplicates. Appendix B details this process in depth.After collecting the seed functions, we perform Self-OSS-Instruct, our adaptation of OSS-Instruct [72] for self-alignment, to generate diverse instructions. In detail, we employ in-context learning to let the base model self-generate instructions from the given seed code snippets. This process utilizes 21 carefully designed few-shot examples listed in Appendix E. The instruction generation procedure is divided into the following two steps:• Concepts extraction: For each seed function, we prompt the base model to produce a list of code concepts present within the function. Code concepts refer to the foundational principles and techniques used in programming, such as pattern matching and data type conversion. • Instruction generation: We then prompt the base model to self-generate a coding task conditioned on the identified code concepts and two additional attributes, difficulty (easy/medium/hard) and category (function/class/program implementation), which we randomly sample to enrich the diversity of the generated instructions.Given the instructions generated from Self-OSS-Instruct, our next step is to match each instruction with a high-quality response. Prior practices commonly rely on distilling responses from stronger teacher models, such as GPT-4, which hopefully exhibit higher quality. However, distilling proprietary models leads to non-permissive licensing and a stronger teacher model might not always be available. More importantly, teacher models can be wrong as well, and the distribution gap between teacher and student can be detrimental.We propose to self-align the base model by explicitly instructing the model to generate tests for self-validation after it produces a response interleaved with natural language. This process is similar to how developers test their code implementations. Specifically, for each instruction, the base model samples multiple outputs of the format (response, tests), and we filter out those responses falsified by the test execution under a sandbox environment. We then randomly select one passing response per instruction to the final instruction tuning dataset.In this section, we list the outputs from different models given the same seeds.[ Seed ] def let_count ( word : str ) -> dict :""" Returns the count of letters in a string as a dictionary """ return { x : word . count ( x ) for x in set ([ x for x in word ]) } 
main evaluation: 
 In this section, we comprehensively evaluate SelfCodeAlign over a diverse set of coding tasks:• Function generation ( §3.1): Given a natural-language description, LLMs are asked to generate a self-contained function whose correctness and efficiency is checked through test execution [8, 3, 38, 21, 76, 39] .• Class generation ( §3.2): Given a code skeleton with both class-and method-level information, LLMs are asked to generate the class and its methods [13] .• Data science programming ( §3.3): Given a description of a data science task and a partial code snippet, LLMs are asked to complete the code snippet to pass corresponding tests [30] .• File-level code editing ( §3.4): Provided with the contents of a file, the model is asked to edit the program following a natural language instruction [6] .Table 1 : Pass@1 (%) of different LLMs on EvalPlus computed using greedy decoding.HumanEval+ MBPP+ Transparent Non-proprietary Non-distilled GPT-4-Turbo [47] Not disclosed 81.7 70.7 Mistral Large [22] Not disclosed 62.8 56.6 Gemini Pro [63] Proprietary 55.5 57.9Llama3-70B-Instruct [14] Proprietary 70.7 66.4 CodeLlama-70B-Instruct [57] Proprietary 65.2 61.7WizardCoder-33B-v1.1 [41] GPT distillation 73.2 66.9 OpenCodeInterpreter-DS-33B [83] GPT distillation 73.8 67.7 Magicoder-S-DS-6.7B [72] GPT distillation 70.7 65.4 DeepSeekCoder-33B-Instruct [19] Not disclosed 75.0 66.7 --CodeQwen1.5-7B-Chat [64] Not disclosed 77.7 67.2 --Snowflake Arctic (480B) [55] Not disclosed 64.3 64.3 --Mixtral-8x22B-Instruct-v0.1 [23] Not disclosed 70.1 62.9 --Command-R+ (104B) [16] Not disclosed 56.7 58.6 --Mixtral-8x7B-Instruct-v0.1 [23] Not HumanEval+ and MBPP+. HumanEval [8] and MBPP [3] are the two most widely-used benchmarks for function-level code generation. We use their test augmented versions, i.e., HumanEval+ and MBPP+, with 80×/35× more test cases for rigorous evaluation [38] .As baselines, we consider a diverse set of state-of-the-art instruction-tuned models over various dimensions, including weight openness, data openness, transparency, and performance. Table 1 compares the pass@1 of the self-aligned SelfCodeAlign-CQ-7B against other baseline models onHumanEval+ and MBPP+. Among those trained using a fully transparent pipeline without any proprietary data or distillation, SelfCodeAlign-CQ-7B stands out as the best LLM by drastically outperforming the base model, OctoCoder-16B, StarCoder2-15B, and CodeQwen1.5-7B-OctoPack. Meanwhile, compared to much larger models, SelfCodeAlign-CQ-7B outperforms Arctic, Command-R+, and Mixtral-8x7B-Instruct, while closely matching Mixtral-8x22B-instruct. Even compared to LLMs trained using proprietary data (e.g., manually annotated), SelfCodeAlign-CQ-7B remains competitive, surpassing Gemini Pro, Mistral Large, and CodeLlama-70B-Instruct. Additionally, SelfCodeAlign-CQ-7B, fine-tuned on purely self-generated data, closely rivals models finetuned with distillation-based or non-transparent synthetic data.LiveCodeBench. In subsequent evaluations, we benchmark our model against state-of-the-art open-source LLMs of similar sizes for a fair comparison. LiveCodeBench [21] is a benchmark for contamination-free evaluation. It features 400 recent Python algorithm challenges from May 2023 to February 2024. These tasks are curated from online judge websites such as Codeforce and LeetCode, each with over 20 test cases on average. While LiveCodeBench is a holistic benchmark covering four problem types, we focus on the code generation task for assessing LLM function generation.Table 2 reports the pass@1 results for problem subsets created after three specific start dates. It shows that SelfCodeAlign-CQ-7B consistently outperforms most baseline models and closely matches CodeQwen1.5-7B-Chat. In addition, moving the start date forward has minimal impact on the pass@1 of SelfCodeAlign-CQ-7B, indicating that our pipeline is less likely to suffer from contamination. EvoEval.To mitigate the impact of potential data contamination, EvoEval [76] includes 828 programming problems created by prompting GPT-4 to evolve original HumanEval tasks across 5 semantic-altering and 2 semantic-preserving benchmarks. Following the leaderboard of EvoEval, we use the 5 semantic-altering benchmarks, each of which has 100 problems.Table 3 shows that SelfCodeAlign-CQ-7B achieves the best pass@1 score among all transparently finetuned models. Meanwhile, it also surpasses most open LLMs (except CodeQwen1.5-7B-Chat) trained on unknown instruction-tuning data. EvalPerf. While the earlier benchmarks focus on code correctness, we use EvalPerf [39] to evaluate the efficiency of LLM-generated code. EvalPerf includes 118 performance-exercising tasks with computation-intensive test inputs to fully exercise the efficiency of LLM-generated code.Since code efficiency only matters when the generated code is correct, in Table 4 we only evaluate baselines that can achieve a decent pass@1 (i.e., over 50%) on HumanEval+. Specifically, we run EvalPerf by following its default settings: (i) Each model generates 100 samples per task at the temperature of 1.0; (ii) We evaluate the efficiency of up to 20 correct samples per model for tasks where it can at least generate 10 passing samples; and (iii) Finally we rank the models based on their win rates, where each model pair compares their differential performance score (DPS) over the common set of passing tasks. Notably, DPS is a LeetCode-inspired metric that indicates the overall efficiency ranking of submissions. For example, Table 4 shows that SelfCodeAlign-CQ-7B achieves a DPS of 79.9, indicating that its correctly generated solutions can overall outperform or match the efficiency 79.9% of reference solutions across various efficiency levels.Table 4 shows that SelfCodeAlign-CQ-7B ranks second among the evaluated models of comparable size. Specifically, SelfCodeAlign-CQ-7B is only next to DeepSeek-Coder-6.7B-Instruct whose training data is not disclosed. Surprisingly, the efficiency of SelfCodeAlign-CQ-7B-generated code surpasses many recent open models trained using private data, including the latest Llama-3.1-8B-Instruct. We evaluate code LLMs on class-level code generation using ClassEval [13] , a collection of 100 class-level Python code generation tasks, covering 100 classes and 410 methods, with an average of 33 tests per class and 8 tests per method.Following the ClassEval paper [13] , we set the maximum model context size as 2048 tokens and report the best class-level pass@1 (and corresponding method-level pass@1) of each model among three generation strategies: (i) Holistic Generation: generating the entire class given a class skeleton, (ii) Incremental Generation: generating class methods iteratively by putting earlier generated methods in the prompt, and (iii) Compositional Generation: generating each class method independently without looking at other methods. Specifically, class-level pass@1 in Table 5 refers to the pass rate of generated classes given both the method-and class-level tests. In contrast, method-level pass@1 is computed by only checking if the generated methods can pass the method-level tests. Table 5 shows, in terms of class-level performance, SelfCodeAlign-CQ-7B is the best transparently finetuned model, surpassing the second-best transparent model (i.e., CodeQwen1.5-7B-OctoPack) by 28%, while performing no worse than those using unknown or proprietary instruction-tuning data. For method generation, SelfCodeAlign-CQ-7B also stands out in transparently finetuned models.DS-1000 [30] is a benchmark of 1000 realistic data science challenges across 7 popular Python data science libraries. In DS-1000, a model must complete a partial code snippet to solve the problem. The solution is then evaluated through test execution. Table 6 shows that SelfCodeAlign-CQ-7B, despite being trained on limited data science code, stands out as the best in the transparent model category, while remaining competitive among the other evaluated baselines. We further evaluate LLMs on code editing tasks using the CanItEdit benchmark [6] , comprised of 210 code editing tasks from three change kinds (70 tasks each): corrective (fixing bugs), adaptive (adding new features), and perfective (improving existing features). The tasks are evaluated based on the correctness of the generated code changes, according to a set of hidden test cases. For each task, the model is given as input the original code snippet and a natural-language instruction describing the desired code change; then it is expected to produce an updated code snippet that satisfies the instruction. We follow the setting from the original benchmark [6] to generate 20 completions per task at a temperature of 0.2. Table 7 reports the pass@1 for each change kind and the average pass@1 across all tasks. Despite not being specifically tuned for code editing, SelfCodeAlign-CQ-7B exhibits strong performance on CanItEdit, achieving a pass@1 of 39.0%, outperforming all other models except CodeQwen1.5-Chat, whose instruction tuning details are not disclosed.  
component analysis: 
 In this section, we extensively study how different components contribute to the SelfCodeAlign pipeline. To make the comparison tractable, we fix a subset of seed code snippets by randomly sampling 37k examples from the 250k corpus and evaluate finetuned models on HumanEval+ [38] .To assess whether SelfCodeAlign is generalizable and how performance varies with finetuning data generated by different models, we run the same data generation pipeline end to end with different LLMs. We include four diverse state-of-the-art model architectures and sizes ranging from 3B to 33B to observe how SelfCodeAlign performs across small, medium, and large-scale LLMs.Table 8 shows the comparison and guides us to reach the following findings. Looking at the diagonal cells, SelfCodeAlign consistently improves the performance of the base models with varying sizes, from 3B to 33B. Comparing each diagonal cell and the cell immediately to its right (i.e., using base models with slightly better HumanEval+ performance as the teacher models), we can see that a base model may benefit more from self-generated data than a stronger teacher model, when they don't have a large performance gap. However, when the teacher model is clearly stronger, the base model learns better by distilling the teacher's knowledge. For example, StarCoder2-3B achieves higher pass@1 trained on its own data (35.4) The SelfCodeAlign pipeline samples multiple responses for an instruction and each response is equipped with self-generated test cases. Responses with failing tests are filtered out and each instruction will be paired with a randomly selected passing response. To answer the question of "to what extent is execution information helpful", in Table 9 , we conduct 4 controlled experiments by varying how responses are selected while keeping the other components unchanged:• Random selection (all): pair each instruction with a random response without response filtering.• Random selection (subset): 15.6k subset of "Random selection (all)" for a consistent data amount.• Failures only: pair each instruction with a failing response.• Passes only: pair each instruction with a passing response. First, we can observe that random pairing performs worse than using only passing examples, both when data sizes are aligned and when they scale up by 1.8×. Meanwhile, the "Failure only" setting results in the worst performance where we deliberately use failing responses for each instruction. These results suggest the importance of execution filtering and code correctness for self-alignment.For instruction generation, SelfCodeAlign applies Self-OSS-Instruct that first selects high-quality seed code snippets, then mines code concepts from the seeds, and finally generates the instructions.To validate the usefulness of concept generation and high-quality seeds, we compare two variants of SelfCodeAlign in Table 10 : 1) directly generating instructions from seeds, where the model produces an instruction based solely on a seed snippet, and 2) using the default pipeline except for the initial seeds, where random snippets are sampled from different code documents in The Stack V1. It is shown that directly generating instructions from seeds leads to the poorest performance. This is because a direct generation from seeds requires the seed snippet to be presented in the context, whose format is not well represented in the wild and may not be in distribution for the model. The generated instructions will then be distracted and thus be of lower quality. Concept generation neutralizes this effect and produces more realistic and natural instructions. Using random snippets produces a more diverse but less coherent set of concepts, leading to slightly worse performance compared to using high-quality seeds. Appendices D.3 and D.4 illustrate some qualitative examples. To compare self-alignment with distillation, we evaluate SelfCodeAlign against two state-of-the-art distillation methods for code instruction tuning: OSS-Instruct [72] and Code Evol-Instruct [65] .We use the official OSS-Instruct dataset. As the official implementation of Code Evol-Instruct is unavailable, we opt for the most popular open-source version [44] on Hugging Face. Both datasets are generated using GPT-3.5-Turbo [46] and we randomly select their subsets to match the 74k samples generated by SelfCodeAlign. Table 11 shows that SelfCodeAlign substantially outperforms both methods, indicating the strength and promising future of self-alignment for code. Additionally, SelfCodeAlign outperforms direct distillation, where we use the same set of SelfCodeAlign instructions but rely on GPT-4o [49] to generate each response at temperature 0. This suggests that weaker models, combined with more post-validation compute, can produce higher-quality responses. 
related work: 
 Instruction tuning for code. To build more powerful code assistants, pre-trained code models are fine-tuned over a small amount of high-quality instruction-response pairs that are either collected from real-world [43] or synthetically generated [7, 57, 41, 72] . This step is known as instruction tuning.OctoPack [43] compiles a large set of real-world Git commits which are partially used for code fine-tuning. Code Alpaca [7] applies SELF-INSTRUCT to the code domain, which prompts ChatGPT to generate synthetic instruction data for code. Similarly, the instruction data for CODELLAMA [57] includes coding problems generated by prompting LLAMA 2 [66] and solutions and tests by prompting base CODELLAMA. Code Evol-Instruct [41] uses harder programming challenges as instruction data to fine-tune more capable models. Specifically, Code Evol-Instruct prompts ChatGPT with heuristics to evolve existing instruction data to more challenging and complex ones. Besides data complexity, the widely-adopted [14, 62, 71] OSS-INSTRUCT [72] looks at the data diversity and quality dimension. Specifically, given a source code snippet, OSS-INSTRUCT prompts ChatGPT to get inspired and imagine potential instruction-response pairs, which inherit the diversity and quality of sampled code snippets. Besides instruction tuning, recent work on training code LLMs for performance improvement also explores multi-turn code generation [83] , model merging [12] , preference tuning [74, 36] , and reinforcement learning [15] . Recently, various strong instruction-tuned code models have been released by major organizations [19, 64] . However, their instruction-tuning recipes (e.g., data and strategies) are not fully disclosed. This lack of transparency underscores the need for fully transparent and permissive instruction-tuning methods to advance the field.Self-alignment. Self-alignment is an approach to instruction tuning that utilizes an LLM to learn from its own output without depending on an existing well-aligned teacher LLM. SELF-INSTRUCT [68] is one of the first endeavors that allow GPT-3 to improve itself by generating new instructions and responses for instruction-tuning using its in-context learning capability. SELF-ALIGN [61] , based on in-context learning, utilizes topic-guided SELF-INSTRUCT for instruction generation and pre-defines principles to steer the LLM towards desired responses. These instruction-response pairs are used to fine-tune the base model, followed by a final refinement stage to ensure the model produces in-depth and detailed responses. Instruction backtranslation [35] offers an alternative self-alignment method by initially training a backward model to generate instructions from unlabeled web documents using limited seed data. It then iteratively produces new instructions from new web documents and selects high-quality data for self-training. Most code LLM work targets knowledge distillation. Haluptzok et al. [20] share a relevant idea to our work but only consider program puzzles specified in symbolic forms. This setting cannot be generalized to real-world tasks with natural language involved.6 Limitations and Future WorkWe limit our data generation within a ∼3000 window, skewing our distribution towards medium-sized samples. Therefore, generating and training on long-context instruction-response pairs can be a promising avenue [4] . Second, we gather several negative samples during response generation, which are currently filtered out. These negatives could be used in a reinforcement-learning loop to steer the model away from incorrect responses [31, 53] . Furthermore, the good responses are labeled by test execution, while the generated unit tests might be erroneous, calling for research to study and improve the generation of valid test cases. Finally, we plan to apply SelfCodeAlign to more challenging domains such as complex program generation [84] and agentic software engineering [26] . 
conclusion: 
 We introduce SelfCodeAlign, the first fully transparent and permissive pipeline for self-aligning code LLMs without extensive human annotations or distillation. SelfCodeAlign-CQ-7B, finetuned from CodeQwen1.5-7B using SelfCodeAlign, outperforms the 10× larger CodeLlama-70B-Instruct on HumanEval+ and consistently surpasses CodeQwen1.5 trained with OctoPack on all studied benchmarks. We evaluate SelfCodeAlign across various model sizes, illustrating that stronger base models benefit more from self-alignment than distillation. We also examine the effectiveness of different components in the pipeline, showing that SelfCodeAlign is better than GPT-3.5 and GPT-4o distillation. Overall, we demonstrate for the first time that a strong instruction-tuned code LLM can be created through self-alignment, without expensive human annotations or distillation.  
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?Answer: [Yes] Justification: We provided a sandbox implementation for code execution during synthetic data generation and self-validation. The sandbox safeguards the LLMs such that execution of LLM-generated code cannot impact beyond the sandbox.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?Answer: [Yes] Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained? Answer: [NA] Justification: This work does not involve crowdsourcing nor research with human subjects. Guidelines:• The answer NA means that the paper does not involve crowdsourcing nor research with human subjects. • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. • We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. • For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review. 
 
 Appendix: import torch def one_hot (y , num_dim =10) : """ One Hot Encoding , similar to ' torch . eye ( num_dim ) . index_select ( dim =0 , index = y ) ' : param y : N -dim tenser : param num_dim : do one -hot labeling from '0 ' to ' num_dim -1 ' : return : shape = ( batch_size , num_dim ) """ one_hot_y = torch . zeros ( y . size (0) , num_dim ) if y . is_cuda : one_hot_y = one_hot_y . cuda () return one_hot_y . scatter_ (1 , y . view ( -1 , 1) , 1.) Listing 1: An example Python seed function from our final dataset.In this section we describe in detail our seed gathering process, which is used to collect the initial set of seed Python functions for the concept extraction phase.We provide an example seed function in Listing 1. All seeds in our dataset take on the format of: imports (if any), signature, docstring, implementation. ( expression_statement ( string ( string_start ) @docstring . start ( string_content ) ( string_end ) @docstring . end ) ) ) ) @function . def (# eq ? @docstring . start " \\\"\\\"\\\" " ) (# eq ? @docstring . end " \\\"\\\"\\\" " ) )Listing 2: The Tree-sitter query utilized for extracting Python functions with docstrings.Our seed gathering process starts off by extracting all Python functions with docstrings from The Stack v1, which is a large dataset of code from GitHub. We accomplish this by utilizing the Tree-sitter parser and the query language it provides. Listing 2 provides the query utilized for matching each function, assuring that the functions live at the top-level of the program, and that they indeed contain a docstring. Utilizing this query, we extracted a total of 5,359,051 Python functions with docstrings. Interestingly, we found that roughly only 20% of Python functions contain any docstring.After gathering our 5M Python functions, we apply a series of filtering and transformations steps. After all of our filtering rules, we are left with a dataset of 248,934 high-quality Python functions. These steps are a generalization of the dataset building pipeline in MultiPL-T [5] , which only manages to produce half as many functions (those without imports). We detail each step of this process below.Import prediction By naively extracting the functions from Python files, we may have lost import statements of external libraries that are utilized inside the function. To remedy this, we utilize the autoimport library to infer potential import statements for unbound identifiers in the function.Removing benchmark data To enable a fair evaluation of our method, we decontaminate our seed dataset from examples that resemble prompts and solutions of items in the benchmarks on which we evaluate on. We accomplish this by checking if either the substring of the solution or prompt to each benchmark item exists in any function in the dataset.Return filtering To aid in our self-validation step, we aim to include only functions that return a value, such that potential responses will contain test cases with complex expected values. We utilize Tree-sitter to filter any function that does not contain at least one return statement with an argument value.Type-checking To further ensure the quality of our Python functions, we apply Pyright, a heuristic type-checker for Python, on all of our functions, and keep only ones passing the check. This step also ensures that no unbound identifiers are referenced inside the function.We find that several Python functions, while having defined a docstring, contain poor or misleading documentation. In aims of removing such functions, we employ StarCoder2-15B with a simple binary classification prompt, tasking the model to detect functions with poor docstrings. We remove all functions that were deemed poor quality by this classifier.Near-deduplication As a final step, we wish to increase the diversity of seeds in our dataset. To accomplish this, we utilize MinHash with Locality-Sensitive Hashing and a Jaccard Similarity threshold of 0.5 to identify duplicate groups of functions in our seed dataset. We then only pick a single function from each group, and add it to our final dataset. We note that this is the same process utilized to deduplicate the pre-training dataset of StarCoder and StarCoder2 [33, 40] .We implement 21 few-shot examples of the form (seed, property, instruction, response, tests), where coding concepts are encoded in the property of each example. Besides coding concepts and programming language, a property includes a task category and a difficulty level that are randomly sampled during data generation. We use eight-shot for concept and instruction generation, and oneshot for response generation. During response generation, we explicitly guide the model to generate tests by concatenating the response and tests in the one-shot example. For the main experiment, if the test case follows a specified format, we additionally include it in the instruction body with a fifty percent chance to boost diversity. Table 12 shows the estimated cost for end-to-end data generation with different models.Throughout the data generation pipeline, we follow [61] and choose a temperature at 0.7 to strike a balance between diversity and quality. We implement a Docker-based execution server for code execution. This sandbox environment includes widely-used Python packages such as Numpy, PyTorch, and Pandas, allowing us to safely execute arbitrary Python code. Additionally, the server supports parallel requests to speed up validation.Our overall hyperparameter choices are derived from existing good practices [41, 35, 72, 83] . We set the initial learning rate at 1e-5 for training on self-generated data and 2e-5 for training on data generated from other models. Empirically, we find this to be the optimal setting for both cases. We adopt a 0.05 warmup ratio and a linear scheduler. We use Adafactor [58] as our optimizer and choose a batch size of 64 with a sequence truncation length of 1280.We primarily conduct data generation, training, and evaluation on a node equipped with 4 NVIDIA A100 PCI-E GPUs, 128 cores, and 512 GB of memory. For experiments involving DeepSeek-Coder, we use a node with 8 NVIDIA H100 GPUs. For DeepSeek-Coder, we utilize DeepSpeed ZeRO-3 [54] for training. For StarCoder2-15B, we use one A100 for training since otherwise it cannot fit the GPU memory due to the extra overhead caused by inter-GPU communication. For all the other experiments, we do a 4-GPU training using PyTorch's Distributed Data Parallel (DDP) module.As indicated in Table 13 , each dataset's license inherits the license of the model that produces it. Additionally, each model's license combines both the base model's license and the data generation model's license. Our source code is licensed under Apache-2.0.[ Seed ] def get_email_domain ( email ) : """ Generates email domain from given email address .""" return " www ."+ email . split (" @ ") [ -1] [ Instruction ] Design a Python class `WordNet `that stores a collection of word terms and their corresponding definitions . You can assume a term is a string and its definition is a list of strings . You should implement the following methods : -`__init__ ( self ) `: Initializes an empty collection of terms and their definitions . -`add_term ( self , term , definition ) `: Adds a new term and its definition to the collection . -`get_definition ( self , term ) `: Retrieves the definition of a given term from the collection . -`get_terms ( self , definition ) `: Retrieves all terms that have the given definition . -`get_term_count ( self ) `: Returns the total number of terms in the collection . -`get_definition_count ( self ) `: Returns the total number of definitions in the collection .The class should handle input validation , such as ensuring terms are non -empty strings and definitions are lists of strings , and raise appropriate errors if invalid inputs are provided .[ Response ] To implement such a class , we can define the class `WordNet `with the required methods . We ' ll use a dictionary to store the terms and their definitions . The `add_term `method will check if the input term and definition are valid and raise appropriate errors if not . The `get_definition `, `get_terms `, `get_term_count `, and get_definition_count `methods will retrieve the corresponding data from the dictionary . Write a Python function `c o u n t _ su b s t r i n g _ i te r a t i v e `that takes two arguments : a string `text `and a string `substring `. The function should iterate through the string `text `and count the number of occurrences of the string `substring `. The function should return the count of non -overlapping occurrences .For example , `c o u n t _s u b s t r i n g _ i te r a t i v e (" abababa " , " aba ") `should return `3 `, since `abababa `contains three non -overlapping occurrences of `aba `: `ababa `, `baba `, and `aba `.[ Response ] The goal is to implement a Python function `c o u n t _s u b s t r i n g _ i te r a t i v e that takes two arguments : a string `text `and a string `substring `. The function should iterate through the string `text `and count the number of occurrences of the string `substring `. The function should return the count of non -overlapping occurrences .Here are the key steps to implement this function :-Initialize a variable `count `to keep track of the number of occurrences . -Loop through the string `text `using a sliding window of the size of the string `substring `: -Check if the current substring of the same size as `substring matches the string `substring `. -If it matches , increment the count .-Move the window to the next position by advancing it by one character .-Return the final count of non -overlapping occurrences .Here ' s how you can implement the `c o u n t _s u b s t r i n g _ i te r a t i v e `function :```python def c o u n t_ s u b s t r i n g _ it e r a t i v e ( text : str , substring : str ) -> int : count = 0 sub_len = len ( substring ) for i in range ( len ( text) -sub_len + 1) : if text [ i : i + sub_len ] == substring : count += 1 return count ``[ Tests ]To test the function , you can use typical test cases such as c o u n t _ su b s t r i n g _ i te r a t i v e (" abababa " , " aba ") `and c o u n t _ su b s t r i n g _ i te r a t i v e (" hello " , " ll ") `.```python assert co u n t _ s u b s t ri n g _ i t e r a t i ve (" abababa " , " aba ") == 3 assert co u n t _ s u b s t ri n g _ i t e r a t i ve (" hello " , " ll ") == 1 assert co u n t _ s u b s t ri n g _ i t e r a t i ve (" mississippi " , " iss ") == 2 ``[ Seed ] def splitLine ( text ) :""" split a line of text on the first space character and return two strings , the first word and the remaining string . This is used for parsing the incoming messages from left to right since the command and its arguments are all delimited by spaces and the command may not contain spaces """ sp = text . split (" ") try : for j in range ( lena = sp [0] b = " ". join (( complex_matrix [0]) ) : assert complex_matrix [ i ][ j ] == converted_matrix [ i ][ j ] ``D .[ ' dictionary comprehension ' , ' set comprehension ' , ' string manipulation '][ Instruction ] Create a Python program that computes the average word length in a text file . The program should read the text from the input file and count the number of words in the file . It should then compute the average word length based on the number of words in the file and the total number of characters in each word .[ Response ] The program can be implemented using the Python `open () `function to read the text file and the `split () `method to split the text into words . The number of words and the total number of characters in each word can be calculated using `len () `and `sum () `functions , respectively . Finally , the average word length can be computed by dividing the sum of the number of characters in each word by the number of words in the file . [ Instruction ] Implement a Python program to read a list of lists containing integers and return a set of all unique numbers . The input list may contain duplicate lists . The output set should not contain any duplicate numbers .[ Response ] We can implement this using a set to keep track of unique numbers . We iterate over each inner list in the input and append its elements to the set . Then , we convert the set back to a list and return it .Here ' s how we can implement this :```python def unique_numbers ( input_list ) : unique_numbers_set = set () for inner_list in input_list : unique_numbers_set . update ( inner_list ) return list ( unique_numbers_set ) ``[ Tests ] Here ' s how we can test this function :```python input_list = [[1 , 2 , 3] , [1 , 2] , [1 , 3] , [1]] assert unique_numbers ( input_list ) == [1 , 2 , 3] ```2 ### StarCoder2 -15 B outputs [ Concepts ][ ' dictionary comprehension ' , ' set comprehension ' , ' string manipulation ' , ' character counting '][ Instruction ] Write a Python function that takes a string as input and returns a dictionary of character counts . The function should return a dictionary comprehension , where the keys are the characters in the string and the values are the number of occurrences of each character .For example , given `input_string = " hello " `, the function should return `{ 'h ': 1 , 'e ': 1 , 'l ': 2 , 'o ': 1} `.[ Response ]We can implement this function using a dictionary comprehension and the `count () `method of the `collections `module . The `count () method returns the number of occurrences of a given element in a list .Here ' s the function : [ Instruction ] Write a Python program that takes a string as input and outputs the number of unique characters in the string . Use a dictionary comprehension to count the occurrences of each character in the string and then convert the keys of the dictionary to a set to count the unique characters .[ Response ] Here is a Python program that takes a string as input and outputs the number of unique characters in the string . It uses a dictionary comprehension to count the occurrences of each character in the string and then converts the keys of the dictionary to a set to count the unique characters :```python def co un t_ u ni qu e_ c ha ra c te rs ( string ) : # Use a dictionary comprehension to count the occurrences of each character in the string character_counts = { char : string . count ( char ) for char in set ( string ) } # Convert the keys of the dictionary to a set to count the unique characters unique_characters = set ( character_counts . keys () )# The number of unique characters is the length of the set return len ( unique_characters ) ``T o test the program , you can call it with some example strings and check that it returns the expected result :```python assert c ou nt _ un iq u e_ ch ar a ct er s (" hello ") == 4 assert c ou nt _ un iq u e_ ch ar a ct er s (" mississippi ") == 4 assert c ou nt _ un iq u e_ ch ar a ct er s ("") == 0 ``[ Tests ] To test the program , you can call it with some example strings and check that it returns the expected result :```python assert c ou nt _ un iq u e_ ch ar a ct er s (" hello ") == 4 assert c ou nt _ un iq u e_ ch ar a ct er s (" mississippi ") == 4 assert c ou nt _ un iq u e_ ch ar a ct er s ("") == 0 ``# ## CodeQwen1 .5 -7 B outputs [ Concepts ] [ ' iterables ' , ' set comprehension ' , ' dictionary comprehension ' , ' string manipulation ' , ' unique character extraction '][ Instruction ] Create a Python class called `StringSetProcessor `that represents a set of strings . The class should have a method èxtract_unique_chars `that takes a list of strings as input and returns a set of all unique characters found in any of the strings . The returned set should be sorted alphabetically .For example , given the list `[" hello " , " world "] `, the function should return `{ 'd ' , 'e ' , 'h ' , 'l ' , 'o ' , 'r ' , 'w '} `.[ Response ]We can implement the `StringSetProcessor `class with a èxtract_unique_chars `method that takes a list of strings as input and returns a sorted set of unique characters from the concatenation of all strings .Here is the implementation : ```python class StringSetProcessor : def extract_unique_chars ( self , strings : list [ str ]) -> set [ str ]: unique_chars = set () for string in strings :unique_chars . update ( string ) return sorted ( unique_chars ) ``T his implementation iterates over each string in the input list , adds all characters from that string to the `unique_chars `set , and then returns the sorted set of these characters .Here [ Instruction ] Write a Python program that creates a dictionary from a given string .The program should take a single string as input and construct a dictionary with each distinct character as a key . The corresponding value for each key should be the number of times the character appears in the input string . The program should output the resulting dictionary . For example , given the input `hello `, the program should output `{ 'h ':1 , 'e ': 1 , 'l ': 2 , 'o ': 1} `.[ Response ]In this example , we will create a dictionary from a given string . The program should take a single string as input and construct a dictionary with each distinct character as a key . The corresponding value for each key should be the number of times the character appears in the input string . The program should output the resulting dictionary . We can use the `collections . Counter `class in Python to solve this problem . This class provides a convenient way to count the frequency of elements in an iterable . In our case , we will convert the input string into an iterable , pass it to `collections . Counter `, and the resulting dictionary will contain the counts for each distinct character in the string .For example :```python from collections import Counter input_string = ' hello ' char_count = Counter ( input_string ) print ( char_count ) # Output : Counter ({ 'l ': 2 , 'h ': 1 , 'e ': 1 , 'o ': 1}) ``Ì n this example , we have created a `Counter `object from the input string `' hello ' `, which contains the counts for each distinct character in the string . The resulting dictionary has the form `{ ' c1 ': count1 , 'c2 ': count2 , ...} `where `c1 `, `c2 `, etc . are the distinct characters in the string , and `count1 `, `count2 `, etc . are their corresponding counts .To complete the program , we can wrap the above code in a function that takes a string as input and returns a dictionary containing the character counts : [ ' string replacement ' , ' dictionary key transformation ' , ' string splitting ' , ' conversion to a valid dictionary key '][ Instruction ] Write a Python program that takes a dictionary that contains key -value pairs with a string value and transforms each key by replacing the string value with a dictionary of key -value pairs where the keys are the individual characters in the string and the values are the indices of those characters in the string . Ensure that the characters in the keys are lowercased , and the numbers in the values are converted to integers .[ Response ] The question is asking us to write a Python program that can accept a dictionary where the values are strings and transform each key by replacing its value with a dictionary that maps each character in the string to its index in the string . We also need to ensure that the characters in the keys are lowercased and the numbers in the values are converted to integers .Here ' s a sample dictionary that we can use as an example : ```python my_dict = { " key1 ": " abc123 " , " key2 ": " ABC " } ``Ẁ e can use the following function to transform the dictionary : ```python def transform_dict ( input_dict ) : ``{ " key1 ": {" a ": 0 , " b ": 1 , " c ": 2 , "1": 3 , "2": 4 , "3": 5} , " key2 ": {" a ": 0 , " b ": 1 , " c ": 2} } ``[ Tests ] You can test the function by providing various dictionaries as inputs and checking if the output dictionary is correct . Here ' s an example :```python my_dict = { " key1 ": " abc123 " , " key2 ": " ABC " } expected_output = { " key1 ": {" a ": 0 , " b ": 1 , " c ": 2 , "1": 3 , "2": 4 , "3": 5} , " key2 ": {" a ": 0 , " b ": 1 , " c ": 2} } actual_output = transform_dict ( my_dict )assert actual_output == expected_output ``# ## DeepSeek -Coder -33 B outputs [ Concepts ][ ' string manipulation for dictionary key ' , ' string replacement '][ Instruction ] Write a Python function to replace the characters of a string by its indices . For example , given the input `" abcde " `, the output should be `"01234" `.[ Response ] To replace the characters of a string by its indices , we can use a for loop to iterate over the characters of the string and concatenate the index of each character to a new string . Here ' s a Python function that accomplishes this : ```python def r ep lac e_c ha rs_ by _in de x ( s ) : new_s = "" for i in range ( len ( s ) ) : [ Instruction ] Write a Python class with methods that return the factorial of an integer and the sum of the square of its digits . Both methods should be well documented and include examples . Assume that the input value is a non -negative integer . Ensure that the output value is validated before returning it .[ Response ]We can implement a Python class with two methods that return the factorial of an integer and the sum of the square of its digits . Both methods should be well -documented and include examples , and we ' ll assume that the input value is a non -negative integer while validating the output value before returning it .Here ' s the Python code for the class :```python class NumericalOperations : """ A class to perform numerical operations on integers .Attributes : num : The input integer .Methods : factorial : Returns the factorial of the input integer . sum_of_squares : Returns the sum of the squares of the digits of the input integer . """ def __init__ ( self , num : int ) :""" Initializes the class with the input integer .Args : num : The input integer . """ self . num = num def factorial ( self ) -> int :""" Returns the factorial of the input integer .The factorial of the input integer . """ if not isinstance ( self . num , int ) or self . num < 0:raise ValueError (" The input integer must be non -negative ") result = 1 for i in range (2 , self . num + 1) : result *= i return result def sum_of_squares ( self ) -> int :"""Returns the sum of the squares of the digits of the input integer .The sum of the squares of the digits of the input integer . """ if not isinstance ( self . num , int ) or self . num < 0:raise ValueError (" The input integer must be non -negative ") result = 0 while self . num > 0:result += ( self . num % 10) **2 self . num //= 10 return result ``T he class represents an instance of a numerical operation with an input integer `num `attribute initialized during instantiation .The two methods `factorial `and `sum_of_squares `perform the requested operations on the input integer , validating the input value , and raising an error when it ' s not a non -negative integer .The class is well -documented with clear method descriptions and examples , as well as detailed docstrings for each method . The code is also concise and efficient , making use of range comprehension and bitwise shifting operations to calculate the factorial while maintaining readability .[ Tests ] To test the functionality of the class , we can instantiate an object and call the methods to verify the expected output . We ' ll also test the exception handling by providing invalid inputs to ensure the error is raised accordingly .```python test_object = NumericalOperations (5) assert test_object . factorial () == 120 assert test_object . sum_of_squares () == 25 try : NumericalOperations ( -1) except ValueError as e : assert str ( e ) == " The input integer must be non -negative " try :NumericalOperations ("5") except ValueError as e : assert str ( e ) == " The input integer must be non -negative " try : NumericalOperations (5. We show a variant of our instruction generation pipeline which directly generates instructions from seeds, along with the outputs produced by the original pipeline.[ Seed ] def _py_not ( a ) : """ Default Python implementation of the " not_ " operator .""" return not a ### Seed -> Instruction [ Instruction ] Design a Python function `not_ `that takes a single argument `a `and returns the negation of `a `. However , the function should be implemented in a non -Pythonic manner , utilizing a custom function `_py_not `that mimics the behavior of the `not `operator in Python . Ensure that the function is efficient and concise .[ ' default Python implementation of the " not_ " operator '][ Instruction ] Design a Python class called `NotOperator `that represents the logical " not " operation in Python . Implement the `__eq__ `method to ensure that two instances of the class are considered equal if their underlying values are the same . Additionally , implement the `__bool__ `method to return the opposite of the underlying value when the object is used in a boolean context .[ Seed ] def is_list ( node : dict ) -> bool : """ Check whether a node is a list node .""" return ' listItem ' in node### Seed -> Instruction [ Instruction ]Implement a Python function `is_list_node ( node ) `that returns `True ìf the given `node `is a list node , otherwise `False `. A list node is a dictionary with a key `' listItem ' `that contains a list value .[ ' dictionary lookup for node type checking ' , ' conditional statement for node type identification '][ Instruction ] Create a Python function that takes a graph represented as a dictionary of nodes , where each node is a dictionary with keys `" type " `and `" children " `, and a target node type `target_type `. The function should return a dictionary of nodes that are of the specified `target_type `, including the target node itself if it matches the `target_type `. The returned dictionary should have the same structure as the input graph .[ Seed ] def i s _ s t a r t _ o f _ n e w _ s e q u e n c e _ i t e m ( line ) : """ Checks whether line is the first line of sequence item data """ return line [0] == '>' ### Seed -> Instruction This section demonstrates examples where random snippets are used as seeds. [ Instruction ] Implement a Python class called `PolicyCalculator `that accepts a dictionary of policies . Each key in the dictionary represents a type of policy , and the value is a list of rules for that policy .The class provides an `add_policy `method to add new policies , and a `get_policy_cost `method that calculates the cost for a given policy type and value .Utilize recursive function definitions , dictionary pattern matching , and assert statements to ensure the correctness of the input data . Additionally , apply policy -based design principles to separate the policy logic from the class implementation , enhancing maintainability and scalability .[ he `__init__ `method initializes the `policies `attribute with the provided dictionary . The `add_policy `method takes the policy type and rules as arguments , performs input validation , and adds the new policy to the `policies `dictionary . The `get_policy_cost method takes a policy type and value as arguments , finds the corresponding rule , and returns its cost . It first checks if the policy type exists in the `policies `dictionary . If the rule is not found , it returns 0.0. [ Instruction ] Create a Python function `generate_classifiers ( classifier_names ) `that takes a list of classifier names as input and returns a list of classifier descriptions . Each classifier description should include the name of the classifier , the language used for implementation , the license details , and the programming language specification . The function should use a natural language to describe each classifier , and the license should be 'MIT '. The programming language specification should be ' Python '.For example , given `classifier_names = [ ' Classifier1 ' , ' Classifier2 '] , the function should return `[ ' Classifier1 is implemented in Python using the MIT license . ' , ' Classifier2 is implemented in Python using the MIT license . '] `.[ Response ] To achieve this functionality , you can use list comprehension to iterate through the list of classifier names and create a list of classifier descriptions . Each classifier description can be created using a string concatenation with the classifier ' s name , programming language , license , and a short description of the classifier .Here ### System : I -> R You are an extremely intelligent AI coding assistant . Please provide an accurate and reliable response to each user instruction . After delivering your response , verify its consistency and correctness by writing a series of executable tests . ### System : C -> I Create a series of independent coding tasks that are original , distinct , diverse , and high -quality , fostering logical thinking . Each task must adhere to specified properties :-category : the type of task ( e . g . , function implementation , class implementation , or program implementation ) -language : the programming language to be used -difficulty : the complexity level of the task ( e . g . , easy , medium , or hard ) -concepts : fundamental principles and techniques the task is designed to incorporate , which developers must understand to effectively solve the task Design the tasks so that the relevant concepts emerge naturally as the most appropriate solutions , without explicitly mentioning that a particular concept should be used .Extract key programming concepts from the provided code snippet .Programming concepts refer to the foundational principles and techniques used in programming , which are crucial for developers to master . List these concepts in a comma -separated format .### System : S -> I Gain inspiration from the given code snippets and create a series of independent coding tasks that are original , distinct , diverse , and high -quality , fostering logical thinking . [ Response ] To encode an integer into a sequence of encoded characters based on the specified criteria , let ' s follow these steps :1. Break down the integer into 5 -bit chunks : Extract 5 -bit chunks from the integer using using `& 31 `( `0 x1F `) , because the binary representation of `31 `( `11111 `) has the last five bits set , which will effectively extract the 5 bits we are interested in . We start from the least significant bits ( LSB ) . 2. Set continuation bit : For all chunks except the last one , set the 6 th bit to 1 using `| 32 `( `0 x20 `) because the binary representation of `32 `( `100000 `) has the 6 th bit set to 1. This indicates that there ' s another continuation chunk following . 3. Shift right : Right -shift the value by 5 to process the next 5 bits . 4. Convert to characters : Use `ord `and `chr `to convert each chunk ( now effectively 6 bits due to the continuation bit ) into a character by adding 63 to its value . This ensures that the character is within a printable ASCII range . 5. Assembly : Assemble these characters into the final encoded string while maintaining the order of the chunks through `reversed `.Here ' s how you can implement this function : Keep in mind that this function is designed for non -negative integers .In Python , right -shifting ( `>>`) a negative integer preserves the sign bit , which could lead to infinite loops or other incorrect behaviors if the same encoding approach is applied to negative integers without appropriate adjustments .[ Tests ] You can validate this function with the following test cases that cover scenarios from zero to a number requiring more than one chunk , ensuring the function operates as expected :```python assert e nc od e _i nt e ge r_ to _ ch ar s (0) == chr ( 63) assert e nc od e _i nt e ge r_ to _ ch ar s (1) == chr (63 + 1) assert e nc od e _i nt e ge r_ to _ ch ar s (31) == chr (63 + 31) assert e nc od e _i nt e ge r_ to _ ch ar s ( int (0 b1 ) ) == chr (0 b1 + 63) assert e nc od e _i nt e ge r_ to _ ch ar s ( int (0 b1000001 ) ) == chr (0 b10 + 63) + chr(63 + 0 b100001 ) ``# ## Example 2 [ Code ] def ceil_shift (n , b ) :""" Return ceil ( n / 2** b ) without performing any floating -point or division operations . This is done by right -shifting n by b bits and incrementing the result by 1 if any '1 ' bits were shifted out . """ if not isinstance (n , int ) or not isinstance (b , int ) : raise TypeError (" unsupported operand type ( s ) : % r and % r " % ( type ( n ) . __name__ , type ( b ) . __name__ ) ) assert n >= 0 and b >= 0 # I haven ' t tested or even thought about negative values mask = (1 << b ) -1 if n & mask : return ( n >> b ) + 1 else :[ Property ] category : function implementation language : Python difficulty : medium concepts : bitwise operation for division , ceiling without division or float , input validation with assertions , non -negative integer checks[ Instruction ] Develop a Python function `c ei l_ d iv id e_ w it ho ut _ di v `that computes the ceiling of `n / 2** b `without using division , floating -point operations , built -in functions like `math . ceil `, or converting it into computing the floor . Instead , use bit manipulation to achieve the result . You write assertions to ensure both `n `and `b `are non -negative integers .[ Response ] We can leverage the properties of bitwise operations to develop a Python function that computes the ceiling of `n / 2** b `without using division , floating -point operations , or any built -in functions like `math . ceil `, and strictly avoiding converting the problem into a floor computation .In the context , dividing by `2** b `is equivalent to right -shifting `n by `b `bits in binary representation when `n `is divisible by 2** b `. Otherwise , this approach inherently computes the floor of the division . To adjust for the ceiling without directly calculating it , we can consider two cases :1. If `n `is divisible by `2** b `, the result of the division is simply the value of right -shifting `n `by `b `bits . 2. Otherwise , we need to increment the right -shifted result by 1 to achieve the ceiling effect .To determine the divisibility of `n `by `2** b `, we can check if the first `b `bits of `n `are all 0. This can be done using a mask with the first `b `bits set to 1 , and then performing a bitwise AND operation with `n `. If the result is 0 , it indicates that `n ìs divisible by `2** b `. The mask can be created using `mask = (1 << b ) -1 `.We ' ll also include assertions to ensure that `n `and `b `are nonnegative integers .Here is the implementation of `c ei l_ di v id e_ w it ho ut _ di v `:```python def ce il _d i vi de _w i th ou t _d iv ( n : int , b : int ) -> int : assert isinstance (n , int ) and n >= 0 , " n must be a non -negative integer " assert isinstance (b , int ) and b >= 0 , " b must be a non -negative integer "mask = (1 << b ) -1 divisible = ( n & mask ) == 0 result = n >> b if not divisible :result += 1 return result ``T his function calculates the ceiling of `n / 2** b `purely through bitwise manipulation and integer arithmetic . The assertions ensure the input constraints are met , as specified .[ Tests ] To verify the correctness of the function , we can compare the results of our function with the expected results obtained using `math . ceil `to ensure the correctness of the ceiling division operation : Implement a Python class with a method `f in d_ nt h _p ri me _ me mo i ze d ( n ) that returns the nth prime number , where n is a positive integer . Utilize memoization to store already found prime numbers in a list within the class to avoid recalculating primes for multiple method calls . Ensure the method efficiently updates and references this list to find new primes as needed . Additionally , enhance performance by avoiding unnecessary prime checks .[ Response ] Implementing the required Python class with the method f in d_ n th _p ri m e_ me mo i ze d ( n ) `involves several key steps :1. The class should have a constructor `__init__ `that initializes the cache list to store the memoized prime numbers . Initially , the list can be seeded with the first two prime numbers , 2 and 3. 2. The method `f in d_ n th _p ri m e_ me mo i ze d `will check if the requested `n `th prime is already in the memoized list . If it is , it will return that prime directly . If not , it will start from the last cached prime and continue to find and cache new primes until the ǹ `th prime is found . 3. A helper method `is_prime `can be defined to check if a number is prime . To efficiently check for primality : -It checks if the number is divisible by each of the primes already in the cache .-It only checks up to the square root of the number to be tested , implemented by comparing the square of the prime with the number .-It skips even numbers by incrementing by 2 for each prime check . This is sound since the cache is initialized with the first ** two ** primes , 2 and 3.Here [ Property ] category : function implementation language : Python difficulty : easy concepts : special formula implementation , odd and even number handling , function compositionHere are two special formulas : [ Property ] category : function implementation language : Python difficulty : medium concepts : decoding compressed data , binary tree traversal , byte array processing$$ f_1 (a , b ) = ( a + 1) \ cdot ( b + 1) -1 $$ $$ f_2 ( k ) = \ begin { cases } \ frac {( k + 1) ^2}{2} + k + 1 & \ text { if } k \ text { is odd } \\ \ frac { k ^2}{2} + 2 k + 1 & \ text { if } k \ text {[ Instruction ] Write a Python function `huffman_decompress `that takes two arguments : `compressed_data `, a byte array of 0/1 sequence representing Huffman compressed data , and `huffman_tree `, a Huffman tree that stores the actual data ( each leaf is associated with a character ) . The function should return the original uncompressed data as bytes `. Define the `HuffmanNode `class yourself as a general tree structure with a `children `list .[ Response ] To accomplish the Huffman decompression , we first need to define the HuffmanNode `class , which , as required , should be a general tree structure with a `children `list and an optional `value `attribute to store the character associated with a leaf node .Then , we ' ll implement the `huffman_decompress `function . This function will traverse the Huffman tree according to the bits in compressed_data `. When it encounters a leaf node , the associated character will be appended to the result , and the traversal restarts from the root of the tree . This process continues until the entire `compressed_data `is processed .You said that `compressed_data `is a byte array of 0/1 sequence , so I assume that each byte , not each bit , represents a step in the Huffman tree . In practice , it is more efficientHere ' s how we can implement this : The method `apply_operation `interprets the operations specified in the form of `"[ opname ]:[ arg ]" `. Here are the detailed formats for each operation , explained through examples : -Prefix addition : `apply_operation (" prefix : Hello , ") -Suffix addition : `apply_operation (" suffix : World !") 64-Lowercase conversion : `apply_operation (" lower :") -Uppercase conversion : `apply_operation (" upper :") -Character replacement : `apply_operation (" replace :a -> e ") The class should handle erroneous inputs gracefully , such as incorrect operation formats or arguments . Additionally , implement the __str__ `method to return a formatted string showing the current value of the string and a list of operations that have been applied to it .[ Response ] The goal is to create a Python class `StringEnhancer `capable of managing a string through various operations , using a central method `apply_operation `. The class also needs to handle errors gracefully and record the operation history . Here are the key steps to implement this class :-Initialize the class with an empty string and an empty list to store the operations . -Inside the `apply_operation `method :-Parse the operation string to determine the operation type and argument ( if any ) .-Gracefully handle any errors in the input format by printing an error message without actually raising an exception .-Execute the correct string manipulation based on the operation .-Append the operation to the records list . -Implement the `__str__ `method by concatenating the current string value and the recorded operations .Here Create a Python function `c a lc u l at e _ to p _ k_ a c cu r a cy `using PyTorch that calculates the top -k accuracy from a batch of predictions and actual labels . It should accept two tensors , `predictions `and targets `, and an integer `k `( default 3) . The function should return the average top -k accuracy as a float , determining if the true label is within the top `k `predictions for each input .[ Response ] The function will take the following parameters as input :-`predictions `: a tensor of shape `( batch_size , num_classes ) `that contains the predicted scores for each class . -`targets `: a tensor of shape `( batch_size ,) `that contains the true class labels for each input . -`k `: an integer representing the number of top predictions to consider for computing the accuracy . The default value should be 3.Here is how you can write this function using PyTorch :```python import torch def ca l c u la t e _t o p _k _ a cc u r ac y ( predictions : torch . Tensor , targets : torch . Tensor , k : int = 3) -> float : # Ensure this function works even if k is greater than the number of classes k = min (k , predictions . size (1) ) _ , top_k_indices = predictions . topk (k , dim =1) target_expanded = targets . unsqueeze (1) . expand_as ( top_k_indices ) correct_predictions = top_k_indices . eq ( target_expanded ) correct = correct_predictions . any ( dim =1) accuracy = correct . float () . mean () return accuracy . item () as arguments . The function should count how many times characters from the end of `str1 `match with characters from the beginning of `str2 `when both strings are traversed simultaneously . The traversal should continue until a pair of characters do not match or until a specific condition influenced by the `strict `flag is met :-If `strict `is `True `, stop counting as soon as a non -matching pair is found . -If `strict `is `False `, skip the non -matching character in `str1 ànd continue matching with the next character .The function should return the total count of matching characters .This implementation requires managing two pointers for the characters in `str1 `and `str2 `, respectively , and using a conditional loop to handle different stopping criteria based on the `strict `parameter .[ Response ]We can use two pointers , one for each string , to iterate over the characters from the end of str1 and the beginning of str2 . The loop continues either until the characters do not match ( for both strict and non -strict cases ) or until it exhausts one of the strings . The `strict `flag controls the behavior when a mismatch is encountered .```python def c o u n t_ c o n d i t i o n a l_ m a t c h e s ( str1 , str2 , strict ) : # Initialize pointers for the end of str1 and the start of str2 i , j = len ( str1 ) -1 , 0 count = 0 # Loop as long as i is non -negative and j is less than the length of str2 while i >= 0 and j < len ( str2 Write a Python function that takes a list from user input and prints the sum of counts for elements that occur more than once . Use a dictionary to count frequencies efficiently . Utilize a list comprehension to extract repeated elements based on the frequency counts , and sum their corresponding counts from the dictionary .[ Response ] To implement this function , we first need to parse the user input as a list of integers and then count the frequency of each element using a dictionary . We can then calculate the sum of the counts for elements that occur more than once .Here (20 , [5 , 10 , 15] ) , (10 , [3 , 6 , 8] ) , (15 , [3 , 6 , 12] ) , (30 , [7 , 14 , 21 , 27] ) , ( APPLE ']) == 0 assert count_matches ([ ' apple ' , ' CaseInsensitive ']) == 0 pass@1, making the results in theory deterministic. pass@1 is commonly used in code LLM papers as it assumes in code completion most users would either accept or reject a completion in one shot. Guidelines: • The answer NA means that the paper does not include experiments. • The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions). • The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors).• It should be clear whether the error bar is the standard deviation or the standard error of the mean. • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 10. Broader Impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [NA] Guidelines: Justification: Our technique is neutral in not implying clear positive or negative impacts on society.• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 


Decision: The paper studies a method that generates synthetic data for instruction finetuning Code LLMs. They first generate a diverse set of data examples, convert and validate them. The authors are able to train a 7B model to match the performance of CodeLlama-70B-Instruct on HumanEval+. The authors addressed the questions and concerns from the reviewers and discussed key design choices of the method. The authors also acknowledged the limitations of generated tests and execution verifications.

Reviews: review: paper_summary: paper_summary: This paper proposes a pipeline for generating synthetic instruction tuning data. The method consists of the following steps: 1. data filtering is applied to seed coding data to select high quality examples; 2. base LLM is used to generate a set of coding concept and category based on the seed data; 3. base LLM is used to generate coding instruction, response and test; 4. generated examples are selected based on the code execution result.
strength_weakness: strength_weakness: While using base model to self-improve is an interesting and useful direction, synthetic data generation could be improved by using a stronger LLM than the base model. It is not clear from the paper whether the proposed framework would be effective compared to previous methods if we use a stronger LLM to synthesize the data. The synthetic data generation could also be potentially improved by having multiple rounds of data generation process.
questions: questions: 1. Have you tried this framework using stronger LLM to generate synthetic data?
2. Can you get even better performance by running several rounds of data generation with improved base model?
limitations: limitations: Yes
paper_summary: paper_summary: This paper introduces SelfCodeAlign, an entirely transparent and permissive pipeline designed for self-aligning code large language models without the need for human annotations or distillation. By applying SelfCodeAlign to CodeQwen1.5-7B, the authors generated a dataset containing 74k instruction-response pairs. They then fine-tuned CodeQwen1.5-7B using this dataset, resulting in SelfCodeAlign-CQ-7B, which demonstrates robust performance on the HumanEval+ benchmark.
strength_weakness: strength_weakness: 1. Lack of Diversity in Generated Tasks: While the method aims to produce a variety of coding tasks, it is unclear how this diversity is achieved or measured. There is a risk that the generated tasks may be biased towards certain types of coding problems, which could limit the model's ability to generalize effectively.
2. Overreliance on Self-Generated Tests: The method relies heavily on tests generated by the model itself to validate responses. This self-validation approach could result in a feedback loop where the model learns to create tests that are easy to pass, rather than generating truly challenging or comprehensive tests. The paper does not address how this potential issue is mitigated.
questions: questions: Refer to the weakness.
limitations: limitations: Refer to the weakness.
paper_summary: paper_summary: The authors proposed SelfCodeAlign that finetunes the model based on the filtered data generated by the same model itself. The authors conduct experiments to show that SelfCodeAlign outperforms most open-sourced models that were finetuned on public code dataset.
strength_weakness: strength_weakness: Compare to models that are distilled/trained on non-disclosed data, the performance of SelfCodeAlign is not as competitive. The presentation can be improved, see **questions**.
questions: questions: 1. Can you properly highlight the row in table 1? 
2. I would suggest to give a brief summarization of the component analysis after line 92.
3. It seems crucial to me to understand why SelfCodeAlign outperforms other dataset (for example, GPT-generated dataset), is there an analysis on how these datasets differ? Also, you mentioned distribution shift across different models in 4.1, is there a qualitative/quantitative comparison between the code generated by different models?
limitations: limitations: Limitations have been discussed.
paper_summary: paper_summary: - The paper introduces SelfCodeAlign, a fully transparent and permissive self-alignment pipeline for code generation in LLMs without relying on extensive human annotations or distillation from larger models. SelfCodeAlign generates instruction-response pairs from seed snippets, evaluates responses with test cases, and fine-tunes models based on successful executions. The approach shows superior performance over state-of-the-art methods, including GPT-3.5-Turbo-based distillation, particularly in HumanEval+ benchmark. The pipeline demonstrates effectiveness across various model sizes, emphasizing the benefits of self-generated data over teacher models with smaller performance gaps. 
- Overall, I feel that SelfCodeAlign is a very easy workflow to follow and I see much potential for such pipelines that do not depend on distillation or human annotations. I recommend an accept.
strength_weakness: strength_weakness: ## Originality
- Perhaps similar to this paper [Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models](https://arxiv.org/pdf/2312.06585) ? Even if it is different, I think that this should also be part of your baseline comparison as well.

## Quality
- The qualitative examples provided in the appendix are excessively long, which may overwhelm the reader and obscure the main differences and contributions of the methodology. It would be beneficial to reduce the number of examples or to shorten them, focusing on highlighting the key differences and improvements over baseline methods. Additionally, the examples are presented in black and white with no descriptions or annotations, making it difficult to discern their significance. Providing clearer, annotated examples with concise explanations would enhance the readability and impact of this section.
- I do not see any weaknesses discussed in this work, for example, in what scenario do you think does this methodology not work? Why is the score still not perfect? (or for eg, below 80% accuracy)
questions: questions: - What about experiments/benchmarking on models that uses the GPT4 family as part of distillation? 
- Why did you only limit it to 33B, what about 70b?
- Line 121: What is the difficulty for? It is subjective, so how does the difficulty aid the model/aid in finetuning of models?
- Line 228-233, Table 7: Any reason why the increasing trend is not consistent, for eg for StarCoder2-15B, the score decreased when tuned with DeepSeek-Coder-33B data? 
- Line 232, Table 7: Why did you not fill up the blank cells just like the last row? This would have ensured that your statement is true for all models, because you are just basing off CodeQwen1.5-7B model only.
limitations: limitations: Limitations are stated. However, I think the authors miss one important point – the reliance on the seed snippets. The performance of the model is based on what it was finetuned on, so if the seed snippets are not sufficiently diverse or representative of the target tasks, then it might have resulted in a large drop in accuracy.

Comments: comment: Thanks for your feedback! We sincerely appreciate the time you took to read our response. If you have any further questions or concerns, please feel free to reach out.
Hi,
Thank you for your thoughtful response. My main concern has been addressed, and I'd like to update my score to a 5.
Thank you for taking the time to read our response. We truly appreciate it! Should you have any new questions or concerns, please don't hesitate to let us know.
Thanks for the response, my concerns are addressed, and I am raising my score.