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e93ef15 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # 多任务训练
我们可以在数据集中添加一个用于标识任务类型的列，并在奖励函数/奖励模型插件中根据任务类型进行判断，从而实现多任务训练。假设数据集中包含数学和编程任务，比如：
```
{"query": "Solve the equation x + 2 = 5", "solution": "3", "task": "math"},
{"query": "Write a function to calculate the Fibonacci sequence", "solution": "xxx", "task": "code"},
{"query": "What is the integral of x^2?", "solution": "xxx", "task": "math"},
{"query": "Implement a sorting algorithm in Python", "solution": "xxx", "task": "code"},
```
我们可以设置不同的奖励函数来分别处理数学数据和代码数据，注意数据集中的列会传入奖励函数，所以我们可以通过 `task` 列
下面是针对不同任务的奖励函数的示例：
```python
from swift.rewards import ORM, orms
import random
# Math-specific reward function
class MathRandomReward(ORM):
def __call__(self, completions, task, **kwargs):
rewards = []
for completion, t in zip(completions, task):
if t == "math":
import random
# imple math accuracy logic
reward = random.random()
rewards.append(reward)
else:
# Return None for non-math tasks
rewards.append(None)
return rewards
# Coding-specific reward function
class CodeRandomReward(ORM):
def __call__(self, completions, task, **kwargs):
rewards = []
for prompt, completion, t in zip(prompts, completions, task):
if t == "code":
# imple coding accuracy logic
reward = random.random()
rewards.append(reward)
else:
# Return None for non-coding tasks
rewards.append(None)
return rewards
orms['math_reward'] = MathRandomReward
orms['code_reward'] = CodeRandomReward
```
对于非当前任务的数据， 通过返回 None 来处理，从而使得奖励相关仅计算任务内的数据。
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