/AWS1/CL_BDKRFTHYPERPARAMETERS¶
Hyperparameters for controlling the reinforcement fine-tuning training process, including learning settings and evaluation intervals.
CONSTRUCTOR¶
IMPORTING¶
Optional arguments:¶
iv_epochcount TYPE /AWS1/BDKEPOCHCOUNT /AWS1/BDKEPOCHCOUNT¶
Number of training epochs to run during reinforcement fine-tuning. Higher values may improve performance but increase training time.
iv_batchsize TYPE /AWS1/BDKRFTBATCHSIZE /AWS1/BDKRFTBATCHSIZE¶
Number of training samples processed in each batch during reinforcement fine-tuning (RFT) training. Larger batches may improve training stability.
iv_learningrate TYPE /AWS1/RT_FLOAT_AS_STRING /AWS1/RT_FLOAT_AS_STRING¶
Learning rate for the reinforcement fine-tuning. Controls how quickly the model adapts to reward signals.
iv_maxpromptlength TYPE /AWS1/BDKRFTMAXPROMPTLENGTH /AWS1/BDKRFTMAXPROMPTLENGTH¶
Maximum length of input prompts during RFT training, measured in tokens. Longer prompts allow more context but increase memory usage and training-time.
iv_trainingsampleperprompt TYPE /AWS1/BDKRFTTRNSAMPLEPERPROMPT /AWS1/BDKRFTTRNSAMPLEPERPROMPT¶
Number of response samples generated per prompt during RFT training. More samples provide better reward signal estimation.
iv_inferencemaxtokens TYPE /AWS1/BDKRFTINFERENCEMAXTOKENS /AWS1/BDKRFTINFERENCEMAXTOKENS¶
Maximum number of tokens the model can generate in response to each prompt during RFT training.
iv_reasoningeffort TYPE /AWS1/BDKREASONINGEFFORT /AWS1/BDKREASONINGEFFORT¶
Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.
iv_evalinterval TYPE /AWS1/BDKRFTEVALINTERVAL /AWS1/BDKRFTEVALINTERVAL¶
Interval between evaluation runs during RFT training, measured in training steps. More frequent evaluation provides better monitoring.
Queryable Attributes¶
epochCount¶
Number of training epochs to run during reinforcement fine-tuning. Higher values may improve performance but increase training time.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_EPOCHCOUNT() |
Getter for EPOCHCOUNT, with configurable default |
ASK_EPOCHCOUNT() |
Getter for EPOCHCOUNT w/ exceptions if field has no value |
HAS_EPOCHCOUNT() |
Determine if EPOCHCOUNT has a value |
batchSize¶
Number of training samples processed in each batch during reinforcement fine-tuning (RFT) training. Larger batches may improve training stability.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_BATCHSIZE() |
Getter for BATCHSIZE, with configurable default |
ASK_BATCHSIZE() |
Getter for BATCHSIZE w/ exceptions if field has no value |
HAS_BATCHSIZE() |
Determine if BATCHSIZE has a value |
learningRate¶
Learning rate for the reinforcement fine-tuning. Controls how quickly the model adapts to reward signals.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_LEARNINGRATE() |
Getter for LEARNINGRATE, with configurable default |
ASK_LEARNINGRATE() |
Getter for LEARNINGRATE w/ exceptions if field has no value |
STR_LEARNINGRATE() |
String format for LEARNINGRATE, with configurable default |
HAS_LEARNINGRATE() |
Determine if LEARNINGRATE has a value |
maxPromptLength¶
Maximum length of input prompts during RFT training, measured in tokens. Longer prompts allow more context but increase memory usage and training-time.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_MAXPROMPTLENGTH() |
Getter for MAXPROMPTLENGTH, with configurable default |
ASK_MAXPROMPTLENGTH() |
Getter for MAXPROMPTLENGTH w/ exceptions if field has no val |
HAS_MAXPROMPTLENGTH() |
Determine if MAXPROMPTLENGTH has a value |
trainingSamplePerPrompt¶
Number of response samples generated per prompt during RFT training. More samples provide better reward signal estimation.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_TRAININGSAMPLEPERPROMPT() |
Getter for TRAININGSAMPLEPERPROMPT, with configurable defaul |
ASK_TRAININGSAMPLEPERPROMPT() |
Getter for TRAININGSAMPLEPERPROMPT w/ exceptions if field ha |
HAS_TRAININGSAMPLEPERPROMPT() |
Determine if TRAININGSAMPLEPERPROMPT has a value |
inferenceMaxTokens¶
Maximum number of tokens the model can generate in response to each prompt during RFT training.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_INFERENCEMAXTOKENS() |
Getter for INFERENCEMAXTOKENS, with configurable default |
ASK_INFERENCEMAXTOKENS() |
Getter for INFERENCEMAXTOKENS w/ exceptions if field has no |
HAS_INFERENCEMAXTOKENS() |
Determine if INFERENCEMAXTOKENS has a value |
reasoningEffort¶
Level of reasoning effort applied during RFT training. Higher values may improve response quality but increase training time.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_REASONINGEFFORT() |
Getter for REASONINGEFFORT, with configurable default |
ASK_REASONINGEFFORT() |
Getter for REASONINGEFFORT w/ exceptions if field has no val |
HAS_REASONINGEFFORT() |
Determine if REASONINGEFFORT has a value |
evalInterval¶
Interval between evaluation runs during RFT training, measured in training steps. More frequent evaluation provides better monitoring.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_EVALINTERVAL() |
Getter for EVALINTERVAL, with configurable default |
ASK_EVALINTERVAL() |
Getter for EVALINTERVAL w/ exceptions if field has no value |
HAS_EVALINTERVAL() |
Determine if EVALINTERVAL has a value |