![](https://csdnimg.cn/release/download_crawler_static/88377021/bgb.jpg)
Table 4: Test Accuracy of QWEN PMP and reward model on diverse human preference benchmark
datasets.
Model
QWEN QWEN Anthropic Anthropic OpenAI Stanford OpenAI
Helpful-base Helpful-online Helpful-base Helpful-online Summ. SHP PRM800K
PMP 62.68 61.62 76.52 65.43 69.60 60.05 70.59
RM 74.78 69.71 73.98 64.57 69.99 60.10 70.52
3.2.2 REINFORCEMENT LEARNING
Our Proximal Policy Optimization (PPO) process involves four models: the policy model, value
model, reference model, and reward model. Before starting the PPO procedure, we pause the policy
model’s updates and focus solely on updating the value model for
50
steps. This approach ensures
that the value model can adapt to different reward models effectively.
During the PPO operation, we use a strategy of sampling two responses for each query simultaneously.
This strategy has proven to be more effective based on our internal benchmarking evaluations. We set
the KL divergence coefficient to 0.04 and normalize the reward based on the running mean.
The policy and value models have learning rates of
1 × 10
−6
and
5 × 10
−6
, respectively. To enhance
training stability, we utilize value loss clipping with a clip value of
0.15
. For inference, the policy
top-p is set to
0.9
. Our findings indicate that although the entropy is slightly lower than when top-p is
set to
1.0
, there is a faster increase in reward, ultimately resulting in consistently higher evaluation
rewards under similar conditions.
Additionally, we have implemented a pre-trained gradient to mitigate the alignment tax. Empirical
findings indicate that, with this specific reward model, the KL penalty is adequately robust to
counteract the alignment tax in benchmarks that are not strictly code or math in nature, such as
those that test common sense knowledge and reading comprehension. It is imperative to utilize
a significantly larger volume of the pretrained data in comparison to the PPO data to ensure the
effectiveness of the pretrained gradient. Additionally, our empirical study suggests that an overly
large value for this coefficient can considerably impede the alignment to the reward model, eventually
compromising the ultimate alignment, while an overly small value would only have a marginal effect
on alignment tax reduction.
3.3 AUTOMATIC AND HUMAN EVALUATION OF ALIGNED MODELS
To showcase the effectiveness of our aligned models, we conduct a comparison with other aligned
models on well-established benchmarks, including MMLU (Hendrycks et al., 2020), C-Eval (Huang
et al., 2023), GSM8K (Cobbe et al., 2021), HumanEval (Chen et al., 2021b), and BBH (Suzgun et al.,
2022). Besides the widely used few-shot setting, we test our aligned models in the zero-shot setting
to demonstrate how well the models follow instructions. The prompt in a zero-shot setting consists
of an instruction and a question without any previous examples in the context. The results of the
baselines are collected from their official reports and OpenCompass (OpenCompass Team, 2023).
The results in Table 5 demonstrate the effectiveness of our aligned models in understanding human
instructions and generating appropriate responses. QWEN-14B-Chat outperforms all other models
except ChatGPT (OpenAI, 2022) and LLAMA 2-CHAT-70B (Touvron et al., 2023b) in all datasets,
including MMLU (Hendrycks et al., 2020), C-Eval (Huang et al., 2023), GSM8K (Cobbe et al., 2021),
HumanEval (Chen et al., 2021b), and BBH (Suzgun et al., 2022). In particular, QWEN’s performance
in HumanEval, which measures the quality of generated codes, is significantly higher than that of
other open-source models.
Moreover, QWEN’s performance is consistently better than that of open-source models of similar size,
such as LLaMA2 (Touvron et al., 2023b), ChatGLM2 (ChatGLM2 Team, 2023), InternLM (InternLM
Team, 2023), and Baichuan2 (Yang et al., 2023). This suggests that our alignment approach, which
involves fine-tuning the model on a large dataset of human conversations, has been effective in
improving the model’s ability to understand and generate human-like language.
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