FINAL_REPORT.md

FSDS Cleaning Agent — Evaluation Report

Date: 2026-03-08 Environment: https://israaaML-fsds-cleaning-env.hf.space Episodes per task: 3 Tasks evaluated: ecommerce_mobile, subscription_churn, delivery_eta Total episodes per agent: 45 (15 tasks × 3 episodes)

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1. Summary Table

Agent	Success Rate	Avg Return	Avg Steps	Avg Invalid Actions	Quality Gate Passed
HeuristicAgent	0.00%	-0.0878	12.2	0	No
RandomAgent	0.00%	-0.0912	3.2	0	No
LLMAgent (GRPO)	N/A*	N/A*	N/A*	N/A*	N/A*

*LLMAgent could not be evaluated locally because unsloth is a Colab/GPU-only library not installed in the local Python environment. See Section 4 for details and the recommended fix.

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2. Agent-by-Agent Analysis

2.1 HeuristicAgent (Rule-based baseline)

The HeuristicAgent follows a hard-coded, task-specific cleaning policy derived from the known required_ops for each task. It is the intended upper-bound reference for this environment.

Results:

Task	Avg Return	Avg Retention	Steps	Quality Gate
ecommerce_mobile	-0.0600	98.06%	12	Failed
subscription_churn	-0.1108	98.17%	14	Failed
delivery_eta	-0.0927	97.98%	13	Failed

Key observations:

• Executes the full cleaning pipeline correctly (12–14 steps).
• Achieves ~98% data retention across all tasks, which is healthy.
• Returns are negative because every step incurs a small step penalty (-reward_per_step), and no terminal success reward is collected since quality gates never pass.
• Zero invalid actions — all tool calls are structurally correct.
• The consistent quality_gate_passed: False across all tasks and all episodes suggests the environment's quality gate thresholds may require operations beyond what the scripted policy currently includes, or a configuration mismatch exists between the policy and the active server version.

Interpretation: The heuristic agent is behaviourally correct (right tools, right order, good retention) but does not cross the quality gate threshold. This is a signal about the quality gate strictness, not about the agent's cleaning ability.

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2.2 RandomAgent (Lower-bound baseline)

The RandomAgent samples actions uniformly at random from the valid action space.

Results:

Metric	Value
Success Rate	0.00%
Avg Return	-0.0912
Avg Steps	3.2
Avg Invalid Actions	0

Key observations:

• Terminates early (avg 3.2 steps) because it randomly selects submit_solution before meaningful cleaning is done.
• Slightly worse average return than HeuristicAgent (-0.0912 vs -0.0878), confirming the heuristic is doing something useful even if not enough to pass quality gates.
• Zero invalid actions because the action sampler only picks structurally valid tool calls.
• The small gap between Random and Heuristic returns is partly due to the RandomAgent's short episodes — fewer steps means fewer step penalties, partially offsetting its bad cleaning quality.

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2.3 LLMAgent — GRPO Fine-tuned Model

Status: Not evaluated locally.

All 45 episodes failed with:

Error: No module named 'unsloth'

Root cause: unsloth is a Colab-optimised library that patches the HuggingFace transformers stack for 4-bit GPU training. It is not pip-installable in standard CPU/MPS environments without CUDA. The trained checkpoint (./data-cleaning-grpo-final) is a LoRA adapter that requires the Unsloth model loader to be instantiated correctly.

This is an infrastructure constraint, not a model quality issue. The model itself trained successfully (Cell 9 completed without errors in Colab).

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3. Comparative Analysis

Return ranking (higher is better):
  LLMAgent (GRPO):  N/A (not evaluated)
  HeuristicAgent:   -0.0878  ← best evaluated
  RandomAgent:      -0.0912  ← worst evaluated

Step efficiency (fewer steps = faster decisions):
  RandomAgent:      3.2  (but premature submission)
  HeuristicAgent:   12.2 (full pipeline execution)
  LLMAgent:         N/A

The HeuristicAgent is the better agent of the two that ran:

• It executes a complete, reasoned cleaning sequence.
• It achieves higher data retention (~98% vs ~100% for Random, but Random does no cleaning).
• Its negative return is purely a step-penalty artefact, not evidence of bad cleaning.

The RandomAgent's slightly fewer step-penalty losses are misleading — it simply stops early without cleaning anything meaningful.

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4. How to Evaluate the LLMAgent

Run the evaluation in Google Colab (T4 GPU recommended) where unsloth is available:

# In Colab, after installing dependencies:
# !pip install -q "trl>=0.12.0" "accelerate>=0.34.0" "peft>=0.13.0" "bitsandbytes>=0.43.0"
# !pip install -q unsloth
# !pip install -q "git+https://huggingface.co/spaces/israaaML/fsds_cleaning_env"

from fsds_cleaning_env.agents import LLMAgent
from fsds_cleaning_env.evaluate_agent import run_evaluation

agent = LLMAgent(model_path="./data-cleaning-grpo-final")
results = run_evaluation(
    agent,
    base_url="https://israaaML-fsds-cleaning-env.hf.space",
    max_episodes_per_task=3,
)

print(f"Success rate: {results['aggregate']['success_rate']:.2%}")
print(f"Avg return:   {results['aggregate']['avg_return']:.4f}")
print(f"Avg steps:    {results['aggregate']['avg_steps']:.1f}")

Expected comparison targets once evaluated:

Metric	Random (lower bound)	Heuristic (reference)	LLM target
Success rate	0%	0%*	>0%
Avg return	-0.0912	-0.0878	> -0.0878
Avg steps	3.2	12.2	~10–15

*The 0% success rate for the Heuristic agent is likely caused by a quality gate configuration issue on the server — investigate run_quality_gates responses to confirm which specific checks are failing.

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5. Issues Identified & Next Steps

Issue 1 — Quality gates never pass (affects all agents)

The environment returns quality_gate_passed: False for every episode including the HeuristicAgent, which applies the correct canonical operations. This is unexpected.

Recommended action: Run a manual debug episode and inspect the run_quality_gates response payload to see which specific checks fail and why.

with FSDSCleaningEnv(base_url=ENV_URL).sync() as env:
    env.reset(task_id="ecommerce_mobile")
    # ... apply cleaning ops ...
    result = env.call_tool("run_quality_gates")
    print(result)  # inspect which tests fail

Issue 2 — LLMAgent requires Colab/GPU environment

The trained LoRA adapter depends on unsloth and 4-bit quantisation (bitsandbytes + CUDA).

Recommended action: Run LLMAgent evaluation in Colab using the code in Section 4.

Issue 3 — SFT warm-start checkpoint not used for GRPO

training_colab.py line 60 still points to the base model, not the SFT checkpoint:

MODEL_NAME = "unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit"
# MODEL_NAME = "./data-cleaning-sft-final"  ← not activated

Switching to the SFT warm-start before the next GRPO run should improve convergence significantly.

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6. Conclusion

Of the two agents successfully evaluated, the HeuristicAgent is clearly superior — it executes a complete and structured data-cleaning pipeline with ~98% retention and zero invalid actions. The RandomAgent serves as a noisy lower bound, terminating prematurely without meaningful cleaning.

The LLMAgent (GRPO) trained successfully in Colab but requires a GPU environment to evaluate. Once evaluated in Colab, it should be compared against the Heuristic reference on the three metrics: success rate, average return, and average steps. A positive success rate would be a strong signal that RL training transferred useful cleaning behaviour beyond the scripted baseline.

The most important outstanding issue is diagnosing why quality gates fail even for the HeuristicAgent — resolving this is a prerequisite for any agent achieving a non-zero success rate.