Tune Gemma 3 1B in JAX with GRPO for reasoning (Part 8): Wrapping Up and Moving Forward

Tunix (Tune-In-JAX)

Conclusion & Next Steps: Wrapping Up and Moving Forward

This is Part 8, the final part of an 8-part series. In Part 7, we evaluated our trained model and measured improvements. Now we’ll recap what we built, troubleshoot common issues, export the final model, and explore where to go next.

Congratulations! 🎉 You’ve completed a comprehensive tutorial on training reasoning capabilities into a language model using GRPO with Tunix on Kaggle’s free TPUs.

Let’s recap what we built, consolidate troubleshooting guidance, export your trained model, and chart the path forward.

What We Built

Over eight parts, we constructed a complete GRPO training pipeline:

┌─────────────────────────────────────────────────────────────────────────┐
│                    COMPLETE TRAINING PIPELINE                           │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   Part 1: Environment Setup                                            │
│   ├── Installed Tunix + dependencies on Kaggle TPU                     │
│   ├── Configured JAX mesh for distributed training                     │
│   └── Set up authentication and directory structure                    │
│                                                                         │
│   Part 2: GRPO Algorithm & Configuration                               │
│   ├── Understood GRPO vs PPO (critic-free, group-based advantages)     │
│   ├── Created TunixTrainingConfig with all hyperparameters             │
│   └── Learned the 4 key GRPO parameters: G, μ, β, ε                    │
│                                                                         │
│   Part 3: Data Loading & Reward System                                 │
│   ├── Built flexible data loader (JSON/CSV/synthetic)                  │
│   ├── Implemented 4-component weighted reward system                   │
│   │   • Format (25%): Tag structure validation                         │
│   │   • Coherence (20%): Step-by-step reasoning detection              │
│   │   • Correctness (55%): Answer accuracy + reasoning depth           │
│   │   • Efficiency (0%): Length appropriateness                        │
│   └── Created Grain pipeline for batching                              │
│                                                                         │
│   Part 4: Model Loading & LoRA Setup                                   │
│   ├── Loaded Gemma 3 1B via Tunix with NNX workaround                  │
│   ├── Applied LoRA for parameter-efficient training (~3% params)       │
│   └── Set up frozen reference model for KL divergence                  │
│                                                                         │
│   Part 5: Reward System Deep Dive                                      │
│   ├── Built RewardAnalyzer for component breakdown                     │
│   ├── Created RewardDebugger for training diagnostics                  │
│   ├── Ran comprehensive test suite before training                     │
│   └── Learned weight tuning for different task types                   │
│                                                                         │
│   Part 6: Training Pipeline                                            │
│   ├── Created optimizer with warmup + cosine decay                     │
│   ├── Built ClusterConfig, RLCluster, GRPOLearner                      │
│   ├── Integrated W&B logging                                           │
│   └── Executed training with composite reward function                 │
│                                                                         │
│   Part 7: Evaluation & Results                                         │
│   ├── Verified LoRA weights were actually updated                      │
│   ├── Established baseline (LoRA B = zeros)                            │
│   ├── Evaluated trained model with 4-component tracking                │
│   └── Compared improvements across all metrics                         │
│                                                                         │
│   Part 8: Conclusion & Next Steps (You are here)                       │
│   ├── Tutorial recap and key takeaways                                 │
│   ├── Troubleshooting guide                                            │
│   ├── Model export for deployment                                      │
│   └── Advanced topics and resources                                    │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Key Takeaways

About GRPO

1. Critic-Free RL: GRPO eliminates the value function by using group-based advantage estimation, reducing memory by ~50%
2. Self-Competition: Responses compete against each other within groups, creating natural learning signal without a learned baseline
3. Key Parameters:

• beta (β): Most critical, controls KL penalty (0.08 works well)
• num_generations (G): More is better but slower (4 is minimum)
• epsilon (ε): Standard PPO clipping (0.2)

About Tunix

1. Component Hierarchy: GRPOLearner → RLCluster → ClusterConfig
2. Separation of Concerns:

• GRPOConfig: Only 4 algorithm parameters
• RLTrainingConfig: Training loop settings
• RolloutConfig: Generation during training

3. Reward Function Signature: (prompts, completions, **kwargs) → List[float]

• completions is nested: List[List[Dict[str, str]]]
• Return flat list of rewards

About the Reward System

1. 4-Component Design: Format + Coherence + Correctness + Efficiency
2. Normalized Scores: All components produce 0.0–1.0 values
3. Weighted Combination: Configurable weights summing to 1.0
4. Weight Tuning for Different Tasks:

• Math focus: Increase correctness weight
• Explanation focus: Increase coherence weight
• Conciseness: Increase efficiency weight

About Training & Evaluation

1. LoRA is Essential: Makes training 1B+ models feasible on limited hardware
2. Gradient Clipping is Critical: max_grad_norm=0.1 prevents KL explosion
3. Component Tracking: Evaluate all 4 components, not just accuracy
4. Debugging Tools: Use reward_analyzer and reward_debugger from Part

Troubleshooting Guide

Common Issues and Solutions

Debugging with Part 5 Utilities

We built powerful debugging tools in Part 5. Use them!

# Diagnose a specific response
reward_debugger.diagnose_response(response, ground_truth)

# Analyze component breakdown
result = reward_analyzer.analyze_response(response, ground_truth, prompt)
reward_analyzer.print_breakdown(result)

# Batch statistics for training monitoring
stats = reward_debugger.batch_statistics(responses, ground_truths)
reward_debugger.print_batch_report(stats)

Pre-Training Diagnostics

Before training, run diagnostics to catch potential problems:

def diagnose_training_issues(config):
    """Check config for common problems before training."""

    issues = []
    warnings = []

    # Check batch size
    if config.train_micro_batch_size > 2:
        warnings.append("Batch size may cause OOM on limited hardware")

    # Check gradient clipping
    if config.max_grad_norm > 0.5:
        warnings.append("Loose gradient clipping may cause instability")

    # Check beta
    if config.beta < 0.04:
        warnings.append("Low beta risks reward hacking")
    if config.beta > 0.15:
        warnings.append("High beta may slow learning")

    # Check reward weights sum to 1.0
    total = (config.format_reward_weight + config.coherence_reward_weight + 
             config.correctness_reward_weight + config.efficiency_reward_weight)
    if abs(total - 1.0) > 0.01:
        issues.append(f"Reward weights sum to {total}, should be 1.0")

    return len(issues) == 0

Exporting the Final Model

After training, export your model for deployment:

def export_model(policy_model, tokenizer, config, output_dir):
    """Export trained model with all metadata."""

    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    # Save LoRA parameters
    lora_state = nnx.state(policy_model, nnx.LoRAParam)
    checkpointer = ocp.StandardCheckpointer()
    checkpointer.save(str(output_dir / "lora_params"), lora_state)
    checkpointer.wait_until_finished()

    # Save configuration
    config_dict = {
        "model_variant": config.model_variant,
        "lora_rank": config.lora_rank,
        "grpo_config": {
            "num_generations": config.num_generations,
            "beta": config.beta,
            "epsilon": config.epsilon,
        },
        "reward_weights": {
            "format": config.format_reward_weight,
            "coherence": config.coherence_reward_weight,
            "correctness": config.correctness_reward_weight,
            "efficiency": config.efficiency_reward_weight,
        },
        "special_tokens": {
            "reasoning_start": config.reasoning_start_token,
            "reasoning_end": config.reasoning_end_token,
            "answer_start": config.answer_start_token,
            "answer_end": config.answer_end_token,
        },
    }

    with open(output_dir / "config.json", "w") as f:
        json.dump(config_dict, f, indent=2)

    # Save evaluation results
    if TRAINED_METRICS:
        results = {"baseline": BASELINE_METRICS, "trained": TRAINED_METRICS}
        with open(output_dir / "evaluation_results.json", "w") as f:
            json.dump(results, f, indent=2, default=str)

    return output_dir

Exporting to Google Cloud Storage

For larger checkpoints or to continue training across sessions, export to GCS:

from google.cloud import storage

def upload_checkpoint_to_gcs(checkpoint_path, bucket_name, destination_path):
    """Upload checkpoint to GCS for persistent storage."""

    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)

    for root, dirs, files in os.walk(checkpoint_path):
        for file in files:
            local_path = os.path.join(root, file)
            relative_path = os.path.relpath(local_path, checkpoint_path)
            blob_path = f"{destination_path}/{relative_path}"

            blob = bucket.blob(blob_path)
            blob.upload_from_filename(local_path)

This is especially useful because Kaggle TPU sessions have a 9-hour limit, and full training may require multiple sessions.

Next Steps & Advanced Topics

Immediate Next Steps

1. Experiment with Hyperparameters

• Try different beta values (0.04, 0.08, 0.12)
• Adjust num_generations (2, 4, 8)
• Vary learning rate and warmup ratio

2. Try Different Datasets

• Use Part 3’s flexible data loader
• Apply to your domain (math, coding, writing)
• Automatic schema detection handles various formats

3. Tune Reward Weights

• Use Part 5’s weight sensitivity analysis
• Adjust for your task priorities
• Monitor component-level improvements

Advanced Topics

┌─────────────────────────────────────────────────────────────────────────┐
│                        ADVANCED DIRECTIONS                              │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   1. Process Reward Models (PRM)                                       │
│      • Train a model to evaluate reasoning STEPS, not just answers     │
│      • Integrate with coherence_reward component                       │
│      • Provides denser training signal                                 │
│                                                                         │
│   2. Multi-Turn Training                                               │
│      • Extend to conversational reasoning                              │
│      • Model learns to ask clarifying questions                        │
│      • Requires conversation reward design                             │
│                                                                         │
│   3. Tool Use Integration                                              │
│      • Train model to use calculator, code execution                   │
│      • Combine reasoning with external verification                    │
│                                                                         │
│   4. GSPO (Token-Level Advantages)                                     │
│      • Group Sequence Policy Optimization                              │
│      • More fine-grained than GRPO                                     │
│      • Available in Tunix: tunix.rl.gspo                              │
│                                                                         │
│   5. Multi-Objective Optimization                                      │
│      • Balance capability with safety                                  │
│      • Use multiple composite rewards                                  │
│      • Pareto-optimal training                                         │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Scaling Up

• Larger Models: Try Gemma 3 4B or 12B with more aggressive LoRA
• More Compute: Use multi-host TPU pods for faster training
• Better Data: Curate domain-specific examples
• Ensemble Rewards: Combine rule-based rewards with learned reward models

Resources & References

Official Documentation

• Tunix GitHub: https://github.com/google/tunix
• Gemma Model Card: https://ai.google.dev/gemma
• Kaggle Gemma Models: https://www.kaggle.com/models/google/gemma-3

Papers & Research

• DeepSeekMath (GRPO Origin): “DeepSeekMath: Pushing the Limits of Mathematical Reasoning” — https://arxiv.org/abs/2402.03300
• PPO (Foundation): “Proximal Policy Optimization Algorithms” — https://arxiv.org/abs/1707.06347
• LoRA: “LoRA: Low-Rank Adaptation of Large Language Models” — https://arxiv.org/abs/2106.09685

Community

• Tunix GitHub Discussions: https://github.com/google/tunix/discussions
• JAX Documentation: https://jax.readthedocs.io/
• Flax NNX: https://flax.readthedocs.io/en/latest/nnx/

The complete code for all 8 parts is available on GitHub.

Series Navigation:

• Part 1: Introduction & Environment Setup
• Part 2: Understanding GRPO Algorithm
• Part 3: Data Loading & Preprocessing
• Part 4: Model Loading & LoRA Setup
• Part 5: Reward Function Design
• Part 6: Training Pipeline
• Part 7: Evaluation & Results
• Part 8: Conclusion & Next Steps ← You are here

All code is available on GitHub. Found this series helpful? Give it a ⭐ and share with others learning about RLHF and reasoning models!