---
title: Optillm
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.6.0
app_file: app.py
pinned: false
license: apache-2.0
---

## References

- [Chain of Code: Reasoning with a Language Model-Augmented Code Emulator](https://arxiv.org/abs/2312.04474) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/plugins/coc_plugin.py)
- [Entropy Based Sampling and Parallel CoT Decoding](https://github.com/xjdr-alt/entropix) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/entropy_decoding.py)
- [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://arxiv.org/abs/2409.12941) - [Evaluation script](https://github.com/codelion/optillm/blob/main/scripts/eval_frames_benchmark.py)
- [Writing in the Margins: Better Inference Pattern for Long Context Retrieval](https://www.arxiv.org/abs/2408.14906) - [Inspired the implementation of the memory plugin](https://github.com/codelion/optillm/blob/main/optillm/plugins/memory_plugin.py)
- [Chain-of-Thought Reasoning Without Prompting](https://arxiv.org/abs/2402.10200) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/cot_decoding.py)
- [Re-Reading Improves Reasoning in Large Language Models](https://arxiv.org/abs/2309.06275) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/reread.py)
- [In-Context Principle Learning from Mistakes](https://arxiv.org/abs/2402.05403) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/leap.py)
- [Planning In Natural Language Improves LLM Search For Code Generation](https://arxiv.org/abs/2409.03733) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/plansearch.py)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/self_consistency.py)
- [Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers](https://arxiv.org/abs/2408.06195) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/rstar.py)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://arxiv.org/abs/2406.04692) - [Inspired the implementation of moa](https://github.com/codelion/optillm/blob/main/optillm/moa.py)
- [Prover-Verifier Games improve legibility of LLM outputs](https://arxiv.org/abs/2407.13692) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/pvg.py)
- [Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning](https://arxiv.org/abs/2405.00451) - [Inspired the implementation of mcts](https://github.com/codelion/optillm/blob/main/optillm/mcts.py)
- [Unsupervised Evaluation of Code LLMs with Round-Trip Correctness](https://arxiv.org/abs/2402.08699) - [Inspired the implementation of rto](https://github.com/codelion/optillm/blob/main/optillm/rto.py)
- [Patched MOA: optimizing inference for diverse software development tasks](https://arxiv.org/abs/2407.18521) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/moa.py)
- [Patched RTC: evaluating LLMs for diverse software development tasks](https://arxiv.org/abs/2407.16557) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/rto.py)