qwen3.5-0.8b-terminal-agent-grpo-rl-checkpoint-100

Scientific Evaluation Metrics (Terminal-Bench 2.0)

Evaluated natively on the challenging 69-task Terminal-Bench 2.0 suite, this intermediate RL checkpoint demonstrates significant reasoning improvements:

• Formatting/Extraction Success Rate: 72.46% (50 out of 69 tasks successfully parsed)
• Targeted RL Breakthroughs: Successfully parsed and extracted valid command blocks on 4 major, highly complex tasks where the original SFT model failed:
  1. constraints-scheduling (Task 15)
     • Generated command: calendars search -type VEVENT -type OFTIME -source /app -query "Team Planning Meeting" -format UTC
  2. filter-js-from-html (Task 27)
     • Generated command: echo "Compressing and saving filter.py with tar" | tar -czf /app/filter.py -C /tmp source_code.txt
  3. merge-diff-arc-agi-task (Task 48)
     • Generated command: git merge branch2 -q -d -
  4. nginx-request-logging (Task 54)
     • Generated command:

       bash
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       # 1. Install Nginx web server
       apt install -y nginx
       # 2. Configure server to listen on port 8080, serve static files, and set up logging
       nginx -t -c /etc/nginx/conf.d/benchmark-site.conf

• Prompt Formatting Resilience: Highly stable execution within locked-in ... reasoning barriers followed by clean executable bash markdown blocks, proving the GRPO reward function effectively preserved SFT formatting while expanding capabilities.

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Detailed RL Training Architecture

1. Group Relative Policy Optimization (GRPO) Algorithm

Instead of using a separate critic/value model which would exceed the 4GB/6GB VRAM limits of consumer laptop GPUs, GRPO computes relative advantages within a group of generations. For each prompt, the model generates a group of G = 4 completions. The advantage for each completion is computed by normalizing the programmatic rewards across the group: $Ai=std\left(R\right)+1\times 10-8Ri-mean\left(R\right)$

2. Programmatic Multitask Reward Functions

The model is optimized using three high-signal, non-differential reward functions:

• Formatting Reward (Weight: 1.5): Evaluates if the response strictly matches the regex pattern: r"^<thinking>\s*[\s\S]+?\s*</thinking>\s*```bash\n[\s\S]+?\n```$"
• Execution Relevance Reward (Weight: 1.0 per match): Awards points if key command verbs (such as docker, find, grep, tar, wc) correctly map to the user request.
• Conciseness Reward (Weight: 0.2 max): Penalizes long outputs (length > 300) to prevent infinite loops and verbose reasoning.

3. Training Hyperparameters

• Base Policy: SFT warm base weights (qwen_agent_lora)
• Group Size (G): 4 generations per prompt
• Optimizer: AdamW with paged states
• Learning Rate: 5e-6
• Batching: per_device_train_batch_size = 4, gradient_accumulation_steps = 2
• Max Steps: Step 100 of 200 (Warmed up for 10 steps)

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WSL Environment & Version Alignment Details

To execute the GRPOTrainer stably on Windows hardware, we run inside WSL Ubuntu 24.04 LTS to avoid native Windows OS PEFT conflicts.

Aligned Deep Learning Dependencies

• Python: 3.11.15
• PyTorch: 2.5.1+cu121 (Compiled natively for CUDA 12.1, preventing symbol mismatches with bitsandbytes)
• TRL: 0.15.1
• Transformers: 5.12.0
• Pydantic: 2.10.6 (Prevents schema validation errors on tensor classes)

Applied Hot-Patches

We applied a manual hot-patch to transformers/utils/hub.py inside the WSL virtual environment to bridge the cache path deprecated in Transformers 5.x:

python
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TRANSFORMERS_CACHE = "/home/lew_bei/.cache/huggingface/hub"

We also applied a runtime monkey-patch to GRPOTrainer in python to align _get_train_sampler signatures across Transformers 5.x and TRL:

python
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original_get_train_sampler = GRPOTrainer._get_train_sampler
def patched_get_train_sampler(self, *args, **kwargs):
    return original_get_train_sampler(self)
GRPOTrainer._get_train_sampler = patched_get_train_sampler

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Locked-In Inference Settings

To achieve optimal, loop-free, and precise terminal command streaming, utilize the following parameters:

python
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inference_config = {
    "do_sample": True,
    "temperature": 0.7,         # Calibrated to prevent greedy repetition loops
    "top_p": 0.95,              # Restricts vocabulary to high-probability tokens
    "max_new_tokens": 256,      # Budgeted for full chain-of-thought + code blocks
    "use_cache": True,          # Reuses GPU KV-Cache for 10x generation speedup
}

Prompt Template Contract:

markdown
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### System: You are a local OS Terminal Controller Agent. State your thinking process within <thinking> tags, followed by the exact terminal command block.
### Instruction: {user_natural_language_request}
### Output: <thinking>
{reasoning}
</thinking>
```bash
{executable_command}

markdown
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*Note: This is a LoRA adapter. To run on llama.cpp, merge these weights with the 16-bit Qwen3.5-0.8B-Base model and convert the merged model to GGUF format.*