agent-rl-benchmark

name	agent-rl-benchmark
description	Agent² RL-Bench: Benchmark for evaluating agentic RL post-training where LLM agents autonomously design, implement, and run complete RL pipelines. Use when evaluating LLM agent capabilities for reinforcement learning engineering, RL pipeline automation, or agentic model alignment.

Agent² RL-Bench: Agentic RL Post-Training Benchmark

Benchmark for evaluating whether LLM agents can autonomously design, implement, and run complete RL pipelines that improve foundation models.

Core Concept

As RL post-training increasingly drives model alignment and specialization, the ability for LLM agents to engineer RL pipelines becomes critical. This benchmark tests:

1. Pipeline Design: Can agents design RL training pipelines?
2. Implementation: Can agents write correct RL code?
3. Execution: Can agents run and debug RL experiments?
4. Improvement: Do the pipelines actually improve models?

Activation Keywords

• Agent² RL-Bench
• agentic RL post-training
• LLM agent RL engineering
• automated RL pipeline
• agent RL benchmark
• RL pipeline automation
• LLM alignment automation

Benchmark Structure

Level 1: Pipeline Design

Agents must design RL pipelines given:

• Target model (base LLM)
• Task specification
• Reward model requirements
• Computational constraints

Outputs:

• RL algorithm selection (PPO, DPO, GRPO, etc.)
• Hyperparameter configuration
• Data preprocessing strategy
• Evaluation protocol

Level 2: Implementation

Agents must implement the designed pipeline:

# Example: GRPO implementation
class AgenticGRPOTrainer:
    def __init__(self, model, config):
        self.model = model
        self.config = config
        
    def generate_responses(self, prompts):
        """Generate multiple responses per prompt"""
        pass
    
    def compute_rewards(self, responses, ground_truth):
        """Reward computation"""
        pass
    
    def compute_grpo_loss(self, log_probs, rewards):
        """Group Relative Policy Optimization"""
        # Advantage = r_i - mean(r)
        # Loss = -mean(log_prob * advantage)
        pass
    
    def train_step(self, batch):
        """Single training step"""
        pass

Level 3: Execution & Debugging

Agents must:

• Execute training runs
• Monitor metrics
• Handle failures
• Debug issues
• Optimize performance

Level 4: Evaluation

Agents must evaluate whether the trained model improved:

def evaluate_model(model, test_tasks):
    metrics = {}
    for task in test_tasks:
        predictions = model.generate(task.inputs)
        metrics[task.name] = task.evaluate(predictions)
    return metrics

Task Categories

1. Reasoning Tasks

• Mathematical reasoning
• Code generation
• Logical deduction
• Chain-of-thought

2. Alignment Tasks

• Harmlessness
• Helpfulness
• Honesty
• Instruction following

3. Specialization Tasks

• Domain adaptation
• Few-shot learning
• Tool use
• Multi-turn conversation

Evaluation Metrics

Pipeline Quality

• Correctness: Does the code run?
• Efficiency: Training time, memory usage
• Scalability: Works on different model sizes?

Model Improvement

• Absolute Gain: Δ in task performance
• Relative Gain: % improvement over baseline
• Generalization: Improvement on held-out tasks
• Robustness: Consistent across seeds

Agent Capabilities

• Autonomy: % tasks completed without human intervention
• Recovery: Ability to recover from failures
• Optimization: Hyperparameter tuning quality

Example Tasks

Task 1: Implement GRPO

Given: Base model, reasoning dataset
Design: GRPO training pipeline
Implement: Complete training loop
Execute: Train for N steps
Evaluate: Pass@1 on math problems

Task 2: Debug RL Training

Given: Failing RL training run
Diagnose: Root cause of failure
Fix: Correct the issue
Verify: Training succeeds

Task 3: Optimize Hyperparameters

Given: Working baseline
Explore: Hyperparameter space
Optimize: For target metric
Report: Best configuration

Difficulty Levels

Level	Description	Example
Easy	Standard RLHF	PPO on preference data
Medium	Advanced methods	GRPO, DPO variants
Hard	Novel problems	Multi-task RL, constrained optimization
Expert	Research-level	Novel algorithm design

Tools Used

• exec: Run RL training scripts
• python: RL implementation
• read: Analysis results, logs
• write: Generate pipeline code

Implementation Guidelines

Agent Environment

class RLBenchEnvironment:
    def __init__(self):
        self.available_models = [...]
        self.compute_budget = {...}
        self.datasets = {...}
    
    def submit_solution(self, agent_code):
        """Submit agent's RL pipeline"""
        return self.evaluate(agent_code)
    
    def get_feedback(self, run_id):
        """Get execution results"""
        return self.runs[run_id]

Success Criteria

A successful agent:

1. Designs valid RL approach
2. Implements bug-free code
3. Trains without crashes
4. Achieves positive Δ performance
5. Generalizes to held-out tasks

References

• arXiv:2604.10547v1 (2026) - "Agent² RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?"
• RLHF literature (Ouyang et al., Bai et al.)
• GRPO (DeepSeek-R1)
• LLM agent benchmarks (SWE-bench, etc.)

Related Skills

• grpo-rl-training: GRPO implementation
• llm-alignment: Model alignment techniques
• agent-evaluation: LLM agent benchmarks
• reinforcement-learning: General RL methods