Papers by Anoop Deoras
LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation (2025.naacl-long)
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Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Recent code completion models focus on local file contexts, but do not fully capture the complexities of real-world software development. |
| Approach: | They propose a version-specific code-completion task across eight libraries as they evolve over the years and an in-depth analysis of two widely used public libraries: PyTorch and Matplotlib. |
| Outcome: | The proposed model improves performance with public libraries, compared with existing models. |
BASS: Batched Attention-optimized Speculative Sampling (2024.findings-acl)
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Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Xiaofei Ma, Anoop Deoras
| Challenge: | Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. |
| Approach: | They propose a batched speculative decoding system that generates sequences at an average speed of 5.8ms per token and a batch size of 8 at a 2.15 speed-up over optimized regular decoding. |
| Outcome: | The proposed system achieves state-of-the-art latency and speed-up over optimized regular decoding. |
Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization (2026.acl-long)
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Yifeng Ding, Hung Le, Songyang Han, Kangrui Ruan, Zhenghui Jin, Varun Kumar, Zijian Wang, Anoop Deoras
| Challenge: | Current reinforcement learning methods suffer from coarse-grained, trajectory-level rewards that provide insufficient learning signals for complex multi-turn interactions, leading to training stagnation. |
| Approach: | They propose a novel RL algorithm for training large language models for multi-turn tool-integrated reasoning (TIR) that incorporates three innovations: turn-level reward assignment that provides fine-grained feedback for individual turns, return-based advantage estimation where normalized discounted returns are calculated as advantages, and self-supervised reward shaping that exploits self-supervision signals from generated code to densify sparse binary outcome-based rewards. |
| Outcome: | The proposed algorithm outperforms GRPO by 3.0% across diverse math reasoning benchmarks and improves grepo by 3.9% on commonsense reasoning and program synthesis tasks. |
CodeFort: Robust Training for Code Generation Models (2024.findings-emnlp)
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Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Existing research efforts to improve code generation models are inadequate . code generation model performance is degraded under small perturbations . |
| Approach: | They propose a framework to improve the robustness of code generation models by generalizing code perturbations to enrich training data and enabling various robust training strategies. |
| Outcome: | The proposed framework increases pass rates and robustness drop rate against code-syntax perturbations. |
Lightweight reranking for language model generations (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) can exhibit considerable variation in quality of sampled outputs. |
| Approach: | They propose a method for reranking LLM generations using pairwise statistics . they show strong improvements for selecting the best k generations for code generation tasks . |
| Outcome: | The proposed approach improves selection and generation quality for code generation tasks and autoformalization, summarization, and translation tasks. |