Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2.3 KiB
Learning Module
The learning module implements learning policies that improve routing, agent,
and tool decisions based on historical interaction outcomes. The module provides
a LearningPolicy ABC taxonomy with specialized sub-ABCs for intelligence
(model routing), agent behavior, and tool selection. It also includes reward
functions for scoring inference results.
Abstract Base Classes
RouterPolicy
::: openjarvis.intelligence._stubs.RouterPolicy options: show_source: true members_order: source
QueryAnalyzer
::: openjarvis.intelligence._stubs.QueryAnalyzer options: show_source: true members_order: source
RoutingContext
RoutingContext is defined in core/types.py.
RewardFunction
::: openjarvis.learning._stubs.RewardFunction options: show_source: true members_order: source
LearningPolicy Taxonomy
The learning system defines a hierarchy of learning policy ABCs:
LearningPolicy-- base ABC for all learning policiesIntelligenceLearningPolicy-- specialization for model routing decisionsAgentLearningPolicy-- specialization for agent behavior adviceToolLearningPolicy-- specialization for tool selection/configuration
Policy Implementations
TraceDrivenPolicy
::: openjarvis.learning.trace_policy.TraceDrivenPolicy options: show_source: true members_order: source
classify_query
::: openjarvis.learning.trace_policy.classify_query options: show_source: true
SFTPolicy
::: openjarvis.learning.sft_policy.SFTPolicy options: show_source: true members_order: source
AgentAdvisorPolicy
::: openjarvis.learning.agent_advisor.AgentAdvisorPolicy options: show_source: true members_order: source
ICLUpdaterPolicy
::: openjarvis.learning.icl_updater.ICLUpdaterPolicy options: show_source: true members_order: source
GRPORouterPolicy
::: openjarvis.learning.grpo_policy.GRPORouterPolicy options: show_source: true members_order: source
Reward Functions
HeuristicRewardFunction
::: openjarvis.learning.heuristic_reward.HeuristicRewardFunction options: show_source: true members_order: source