ParaStudent: Closing the Sim2Real Gap in User Simulators for AI Tutor Evaluation

arXiv:2507.12674 · cs.CY, cs.AI, cs.SE · Submitted 2025-07-16 · Read on arXiv

cs.CY, cs.AI, cs.SE

Submitted: 2025-07-16

Updated: 2026-09-01

Code: https://github.com/mmiroyan/ParaStudent

Terminology

• Fine-Tuning — 28× in this paper · explained in 5 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• Embeddings — 6× in this paper · explained in 2 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Abstract Syntax Tree — 2× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Computer Science — 2× in this paper
• Data Pruning — 2× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Differentially Private — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Intelligent Tutoring Systems — 2× in this paper
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Principal Component — 1× in this paper
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Teaching Assistant — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Evaluating Large Language Models Trained on Code
• DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
• Qwen2.5-Coder Technical Report
• A Survey on Large Language Models for Code Generation
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
• CodeMonkeys: Scaling Test-Time Compute for Software Engineering
• The Llama 3 Herd of Models
• An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation
• CoIR: A Comprehensive Benchmark for Code Information Retrieval Models
• Is Self-Repair a Silver Bullet for Code Generation?
• CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval
• StarCoder 2 and The Stack v2: The Next Generation
• Training Software Engineering Agents and Verifiers with SWE-Gym
• MemGPT: Towards LLMs as Operating Systems
• An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation
• Code Llama: Open Foundation Models for Code
• LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
• Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
• Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models

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