119 lines
4.8 KiB
Markdown
119 lines
4.8 KiB
Markdown
# Code Generation Agent
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This extension adds an execution-feedback loop for Python algorithm generation while keeping the original MiniMind model and checkpoint format unchanged.
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## Architecture
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```mermaid
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flowchart LR
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A[Algorithm task] --> B[MiniMind or OpenAI-compatible model]
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B --> C[Python code extraction]
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C --> D[AST policy validation]
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D --> E[Isolated subprocess]
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E --> F[Deterministic tests]
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F --> G[Structured feedback]
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G -->|repair prompt| B
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F --> H[Verifiable reward]
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H --> I[GRPO or CISPO]
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F --> J[pass@k and error metrics]
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```
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The implementation is split into small, testable modules:
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- `code_agent/verifier.py`: code extraction, AST policy checks, subprocess execution, timeout and structured results.
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- `code_agent/reward.py`: transparent reward components and grouped GRPO scoring.
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- `code_agent/agent.py`: generate-execute-feedback-repair loop with hidden-test-safe feedback.
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- `code_agent/evaluation.py`: compile rate, execution rate, test pass rate, error counts and unbiased pass@k.
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- `code_agent/dataset.py`: JSONL code tasks adapted to the MiniMind chat template.
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- `code_agent/backends.py`: OpenAI-compatible inference adapter for MiniMind, vLLM and SGLang.
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## Task format
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Each JSONL record contains a prompt, a required function name, and JSON-serializable tests:
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```json
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{"task_id":"add-two","prompt":"Implement add(a, b).","entry_point":"add","tests":[{"args":[1,2],"expected":3}]}
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```
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`dataset/code_rl_mini.jsonl` contains four smoke-test tasks. It is deliberately too small for a meaningful training claim; use it to validate the pipeline before preparing a larger train/validation/test split.
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## Evaluate saved candidates
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```bash
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python scripts/eval_codegen.py \
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--tasks dataset/code_rl_mini.jsonl \
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--predictions tests/fixtures/code_rl_predictions.jsonl \
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--ks 1 2
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```
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The report separates:
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- `compile_rate`: candidate passed syntax and policy validation;
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- `execution_rate`: candidate completed without a timeout or runtime exception;
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- `test_pass_rate`: fraction of deterministic tests passed;
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- `pass@k`: probability that at least one of `k` sampled candidates is correct;
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- `status_counts`: syntax, policy, timeout, runtime and wrong-answer failure modes.
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## Run the repair agent
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Start the repository's OpenAI-compatible service, then run:
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```bash
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python scripts/run_code_agent.py \
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--tasks dataset/code_rl_mini.jsonl \
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--base-url http://localhost:8998/v1 \
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--model minimind \
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--max-attempts 3
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```
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Hidden-test details are not sent back to the model by default. Add `--reveal-test-details` only for public development tests.
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## Train with verifiable rewards
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From the repository root:
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```bash
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cd trainer
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python train_grpo.py \
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--reward_type code \
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--data_path ../dataset/code_rl_mini.jsonl \
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--num_generations 6 \
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--loss_type grpo \
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--from_weight full_sft
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```
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The training log includes `degenerate_group_rate`. A group whose candidates all receive the same reward has zero normalized advantage and contributes no useful policy-gradient signal. This metric makes the failure mode visible before introducing strategies such as dynamic sampling or harder tasks.
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## Reward design
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The default reward is intentionally transparent and bounded to `[-1, 1]`:
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| Outcome | Reward |
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|---|---:|
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| all tests passed | 1.0 |
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| wrong answer | partial credit from test pass rate |
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| runtime error | -0.4 |
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| timeout | -0.6 |
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| syntax error | -0.8 |
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| missing code or policy violation | -1.0 |
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Keep hidden tests separate from the prompts and report reward distributions by task difficulty. Otherwise the system can overfit public tests or produce groups with all-identical rewards.
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## Safety boundary
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The local verifier uses AST checks, a temporary working directory, an isolated Python process, a timeout, and POSIX resource limits. These are defense-in-depth measures, not a secure sandbox. Run hostile or externally submitted code inside a dedicated container or micro-VM with network disabled, a read-only filesystem, strict CPU/memory/process limits, and disposable credentials.
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## Experimental protocol
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Use the same held-out tasks and sampling parameters for three comparisons:
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1. single-pass generation baseline;
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2. inference-time execution feedback and repair;
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3. execution-reward GRPO/CISPO followed by the same inference procedure.
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Record pass@1, pass@k, test pass rate, compile rate, timeout rate, average attempts, average generation length, reward mean/std, KL to the reference model, and degenerate-group rate. Model results should only be added to the resume after this protocol has been run on AutoDL and the raw logs are retained.
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## Attribution
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This repository is a derivative of [jingyaogong/minimind](https://github.com/jingyaogong/minimind) under Apache-2.0. The extension preserves upstream model names and checkpoint compatibility; presentation-layer identity prompts are kept neutral.
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