rl-anything-dynamic

name	rl-anything-dynamic
title	RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2602.02488
keywords	["Reinforcement Learning","Dynamic Environments","Reward Modeling","Self-Improvement","Agents"]
description	Enable simultaneous optimization of environment difficulty, policy, and reward model. System uses reward model evaluations to guide environment adaptation, creating positive feedback loop for scalable agent improvement.

RLAnything: Closed-Loop Dynamic RL System

Problem

Standard RL assumes fixed environments. Agent training is sensitive to environment difficulty—too easy provides no learning signal, too hard creates overwhelming noise.

Reward models may misalign with actual performance. Manual environment curation scales poorly.

Core Concept

RLAnything implements closed-loop optimization where environment, policy, and reward model continuously strengthen each other. The reward model's error patterns guide the environment adaptation agent to create appropriately-difficult tasks.

Reward precision requirements (μ > 1, where μ relates to positive/negative task balance) drive dynamic difficulty adjustment, enabling automatic curriculum learning.

Architecture Overview

• Policy Component: Learns from outcome and process rewards combined
• Reward Model Component: Optimizes via consistency with trajectory quality
• Environment Adapter: Uses error patterns to adjust task difficulty
• Outcome Rewards: Global task success/failure signal
• Process Rewards: Step-level quality signals
• Theory-Motivated Balancing: Active learning from reward model behavior

Implementation

Step 1: Design Reward Signal Combination

Merge outcome and process rewards for balanced learning.

def compute_combined_reward(outcome_reward, process_rewards, lambda_param=0.2):
    """Combine outcome and process rewards for trajectory."""
    # outcome_reward: scalar success/failure
    # process_rewards: per-step quality signals

    # Weighted combination
    num_steps = len(process_rewards)

    combined = outcome_reward + (lambda_param / num_steps) * sum(process_rewards)

    return combined

def train_policy_with_combined_rewards(model, trajectories, outcomes, process_rewards):
    """Train policy using combined reward signals."""
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

     trajectory, outcome, proc_rewards  (trajectories, outcomes, process_rewards):
        
        total_reward = compute_combined_reward(outcome, proc_rewards, lambda_param=)

        
        log_probs = model.compute_trajectory_log_probs(trajectory)

        
        loss = -total_reward * log_probs.()
        loss.backward()

    optimizer.step()