Intensive Reward Design Strategies
To address the problem of reward sparsity in multi-round dialogs, Verifiers proposesPhased Incentive Design Program::
- Process incentives: By
MultiTurnEnv(used form a nominal expression)env_responsemethod returns the intermediate reward - grammar check: in
RubricConfigure JSON format validation and other base incentives in the - Courses of Study: start with
SingleTurnEnvTrain basic competencies before migrating to multi-round environments
Specific implementation:
- define
StepRewardIntermediate indicators such as coherence of the computational dialogues - utilization
vf.RubricCombine multiple reward functions (process reward weights of 0.3-0.5 are recommended) - pass (a bill or inspection etc)
vf-evalCommand line tool to monitor reward distribution in real time - Use of long-term mandates
gamma=0.9The discount factor balances immediate/future rewards
Experiments show that the method enables the agent to obtain an effective learning signal within 50-100 iterations.
This answer comes from the articleVerifiers: a library of reinforcement learning environment tools for training large language modelsThe
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