Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Noise Contrastive Alignment of Language Models with Explicit Rewards
Authors: Huayu Chen, Guande He, Lifan Yuan, Ganqu Cui, Hang Su, Jun Zhu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methods in both reward and preference settings with Mistral-8 7B and 7B models. Experiments suggest that Info NCA/NCA surpasses various preference baselines when reward datasets are available. We also find NCA significantly outperforms DPO in complex reasoning tasks like math and coding. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Tsinghua University 2Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University 3Zhongguancun Laboratory, Beijing, China |
| Pseudocode | Yes | Py Torch code for the Info NCA/NCA loss for reward datasets is provided below: def reward_loss(pi_logps, ref_logps, rewards, alpha, beta, loss_type): |
| Open Source Code | Yes | Code: https: //github.com/thu-ml/Noise-Contrastive-Alignment. |
| Open Datasets | Yes | We consider Ultra Feedback [9], an instruction-following dataset annotated by GPT-4. This dataset comprises 64k instructions. |
| Dataset Splits | No | The paper trains models on specific datasets (Ultra Feedback, Ultra Interact) and evaluates them on separate benchmarks (MT-bench, Alpaca Eval). It specifies training parameters like epochs and batch size, but it does not explicitly provide a dedicated validation split from its primary training datasets that is used for hyperparameter tuning or early stopping during training. |
| Hardware Specification | Yes | Experiments are run on Nvidia A40 or RTX 4090 GPUs using bfloat16 precision. |
| Software Dependencies | No | The paper mentions "Py Torch code" and refers to using "Transformer Reinforcement Learning (TRL) library [41] and Zephyr s official code base [40]". However, it does not provide specific version numbers for PyTorch, TRL, or the Zephyr codebase, which is required for reproducible software dependencies. |
| Experiment Setup | Yes | We ablate β {3e 4, 1e 3, 3e 3, 1e 2, 3e 2, 1e 1, 3e 1, 1.0} and α {0.01, 0.1, 0.33, 1.0, 3.33}. The default reward temperature α is 0.01. The default parameterization coefficient β is also 0.01. We adopt the QLo RA [10] fine-tuning technique with rank 16, αlora = 16, and a dropout rate of 0.05. We train all models for 1 epoch. The batch size is 32. We use an Adam W optimizer with a learning rating of 5e-6. |