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Saayan Mitra

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4 papers

2 author rows

TMLR Journal 2025 Journal Article

CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement

Leitian Tao
Xiang Chen
Tong Yu
Tung Mai
Ryan A. Rossi
Yixuan Li
Saayan Mitra

Large Language Models (LLMs) have revolutionized code generation but are require significant resources and tend to over-generalize, limiting their task-specific efficiency. Fine-tuning smaller, open-source LLMs is a cost-effective alternative, yet standard supervised approaches rely solely on correct examples, overlooking valuable insights from failures. We introduce CodeLutra, a new framework that leverages both correct and incorrect code attempts. Instead of purely instructing with correct solutions, CodeLutra uses iterative preference-based refinement, comparing successful and failed outputs to better approximate desired results. This process narrows the performance gap with state-of-the-art, larger models, without requiring massive datasets or auxiliary models. For example, on a challenging data science coding task, using only 500 samples improved Llama-3-8B’s accuracy from 28.2% to 48.6%, approaching GPT-4’s level. By capitalizing on both successes and mistakes, \textsc{CodeLutra} offers a scalable, efficient path to high-quality code generation, making smaller open-source models more competitive with leading closed-source alternatives.

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ICLR Conference 2024 Conference Paper

ToolChain: Efficient Action Space Navigation in Large Language Models with A Search

Yuchen Zhuang
Xiang Chen 0010
Tong Yu 0001
Saayan Mitra
Victor S. Bursztyn
Ryan A. Rossi
Somdeb Sarkhel
Chao Zhang 0014

Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can interact with diverse tools (e.g., functional APIs) and generate solution plans that execute a series of API function calls in a step-by-step manner. The multitude of candidate API function calls significantly expands the action space, amplifying the critical need for efficient action space navigation. However, existing methods either struggle with unidirectional exploration in expansive action spaces, trapped into a locally optimal solution, or suffer from exhaustively traversing all potential actions, causing inefficient navigation. To address these issues, we propose ToolChain*, an efficient tree search-based planning algorithm for LLM-based agents. It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan. By incorporating the A$^*$ search algorithm with task-specific cost function design, it efficiently prunes high-cost branches that may involve incorrect actions, identifying the most low-cost valid path as the solution. Extensive experiments on multiple tool-use and reasoning tasks demonstrate that ToolChain* efficiently balances exploration and exploitation within an expansive action space. It outperforms state-of-the-art baselines on planning and reasoning tasks by 3.1% and 3.5% on average while requiring 7.35x and 2.31x less time, respectively.

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ICML Conference 2023 Conference Paper

Flash: Concept Drift Adaptation in Federated Learning

Kunjal Panchal
Sunav Choudhary
Subrata Mitra
Koyel Mukherjee
Somdeb Sarkhel
Saayan Mitra
Hui Guan 0001

In Federated Learning (FL), adaptive optimization is an effective approach to addressing the statistical heterogeneity issue but cannot adapt quickly to concept drifts. In this work, we propose a novel adaptive optimizer called Flash that simultaneously addresses both statistical heterogeneity and the concept drift issues. The fundamental insight is that a concept drift can be detected based on the magnitude of parameter updates that are required to fit the global model to each participating client’s local data distribution. Flash uses a two-pronged approach that synergizes client-side early-stopping training to facilitate detection of concept drifts and the server-side drift-aware adaptive optimization to effectively adjust effective learning rate. We theoretically prove that Flash matches the convergence rate of state-of-the-art adaptive optimizers and further empirically evaluate the efficacy of Flash on a variety of FL benchmarks using different concept drift settings.

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AAAI Conference 2022 Conference Paper

DiPS: Differentiable Policy for Sketching in Recommender Systems

Aritra Ghosh
Saayan Mitra
Andrew Lan

In sequential recommender system applications, it is important to develop models that can capture users’ evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i. e. , heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies.