PARL

PARL: Parallel-Agent Reinforcement Learning

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**⚠️ Disclaimer**: This is an **open-source community implementation** of the PARL (Parallel-Agent Reinforcement Learning) technique based on the Kimi K2.5 technical report. This is **NOT an official implementation** from Kimi AI or any affiliated organization. This project is maintained independently by The Swarm Corporation and the open-source community.

Open-source implementation of **PARL (Parallel-Agent Reinforcement Learning)**, a novel training paradigm that enables AI models to decompose complex tasks into parallel subtasks and coordinate multiple agents simultaneously.

Overview

PARL is a training methodology that addresses the critical challenge of **serial collapse** in multi-agent systems, where models default to sequential execution despite having parallel computational capacity. By implementing staged reward shaping and a latency-oriented evaluation metric, PARL trains models to efficiently orchestrate up to 100 sub-agents across 1,500+ coordinated steps.

Key Features

• Staged Reward Shaping: Dynamic reward annealing that encourages parallelism early in training and gradually shifts focus toward task success
• Instantiation Reward: Incentivizes subagent creation and concurrent execution
• Critical Steps Metric: Latency-oriented evaluation inspired by parallel computation's critical path concept
• Differentiable Components: Fully compatible with gradient-based optimization
• Orchestrator-Subagent Architecture: Trainable coordinator with frozen execution agents

Architecture

┌─────────────────────────────────────────────┐
│         Orchestrator Agent                  │
│  (Trainable Central Coordin