Fine-tune an Open-Weight Model on Amazon Bedrock to Generate EDA Tcl Scripts with Reinforcement Fine-Tuning

Semiconductor and Electronic Design Automation (EDA) customers drive their design tools (Synopsys, Cadence, Siemens) largely through Tcl (and Perl). General-purpose code models are strong at Python but noticeably weaker at tool-specific Tcl idioms — producing plausible-but-wrong scripts that, in a design flow, are worse than none.

Because Tcl correctness is programmatically verifiable ("does the script parse and run?"), this is a textbook fit for reinforcement fine-tuning (RFT), which AWS documentation explicitly recommends for "code generation using rule-based graders." This best practice shows how a TAM can help an EDA customer stand up an RFT job on Amazon Bedrock that improves an open-weight model's Tcl generation — while keeping proprietary design methodology inside the customer's own AWS account.

RFT for open-weight models (GPT-OSS and Qwen3-32B) became available on Amazon Bedrock in mid-February 2026.

Solution overview

The workflow: prepare a JSONL dataset of Tcl prompts → upload via the OpenAI-compatible Files API → launch an RFT job against an open-weight base model → Bedrock generates multiple candidate responses per prompt and scores each with your AWS Lambda reward function → GRPO optimizes the model toward higher reward → run inference on the fine-tuned model through the same OpenAI-compatible API.

Best Practice

1. Confirm scope and Region. RFT for open-weight models is available for openai.gpt-oss-20b and qwen.qwen3-32b, both in US West (Oregon), us-west-2, via the OpenAI-compatible endpoint bedrock-mantle.us-west-2.api.aws. Choose Qwen3-32B for broader code coverage (recommended here) or GPT-OSS-20B as the alternative.

2. Prepare the training dataset. Bring prompts in JSONL, OpenAI chat-completion format, uploaded with purpose fine-tune. Minimum 100 records (max 20,000); start with 100–200 and scale. Each example has a messages array (system + user prompt) and a reference_answer your reward function uses to score the response:

   {"messages":[{"role":"system","content":"You generate Tcl for <EDA tool>"},
                {"role":"user","content":"Create a false path from clkA to clkB"}],
    "reference_answer":{"expected_commands":["set_false_path"]}}

3. Upload the dataset with the Files API using the OpenAI SDK pointed at the Bedrock endpoint; capture the returned file ID.

   from openai import OpenAI                       # OPENAI_BASE_URL -> bedrock-mantle.us-west-2.api.aws
   client = OpenAI()
   f = client.files.create(file=open("tcl_prompts.jsonl","rb"), purpose="fine-tune")
   training_file_id = f.id

4. Define the reward function (the core of the workflow). RFT scores responses with a custom AWS Lambda grader. For an objective task like Tcl, implement a rule-based grader that: (a) checks the generated Tcl parses (e.g., a tclsh syntax check), (b) optionally lints/dry-runs it, and (c) rewards use of the correct tool commands from reference_answer. Return an aggregate_reward_score plus a metrics_list. Increase the Lambda timeout from the 3-second default toward the 15-minute maximum for heavier checks.

def lambda_handler(event, context):
    results = []
    for item in event:
        tcl = item["messages"][-1]["content"]                 # assistant response
        ref = item.get("metadata", {}).get("reference_answer", {})
        syntax_ok = tcl_parses(tcl)                            # e.g. `tclsh -n`
        cmd_score = command_overlap(tcl, ref)                  # expected commands present?
        score = 0.6 * float(syntax_ok) + 0.4 * cmd_score
        results.append({
            "id": item["id"],
            "aggregate_reward_score": score,
            "metrics_list": [
                {"name": "syntax_valid",     "value": float(syntax_ok), "type": "Reward"},
                {"name": "command_accuracy", "value": cmd_score,        "type": "Metric"},
            ],
        })
    return results

5. Launch the RFT job using the OpenAI-compatible fine-tuning API, specifying the base model, the uploaded dataset file ID, and the reward function. Bedrock runs the training loop using Group Relative Policy Optimization (GRPO) — generating multiple responses per prompt and optimizing toward the reward.

6. Monitor training. Track job events, reward metrics, and checkpoints via the fine-tuning APIs. Watch for the warning signs the docs call out — rewards plateauing below 0.15, rising reward variance, or validation performance declining (overfitting).

7. Run inference and evaluate. Once complete, call the fine-tuned model directly through Bedrock's OpenAI-compatible Responses / Chat Completions APIs — no separate deployment step. Measure the percentage of generated Tcl scripts that pass a clean tclsh parse, base model vs. fine-tuned, to quantify the gain (docs cite up to ~66% average accuracy improvement across RFT use cases).

Data handling: Keep all example prompts and scripts generic — do not include a customer's proprietary Tcl, real project names, or account IDs. During RFT, customer data stays within the AWS governed environment and is not used to train base models.

References

• Reinforcement fine-tuning in Amazon Bedrock
• Fine-tune open-weight models using OpenAI-compatible APIs
• Setting up reward functions for open-weight models
• Prepare data for open-weight models
• Generative AI for Semiconductor Design and Verification (AWS for Industries)