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switch eval to use managed server adapter impl. moved managed server

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adapter
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balyan.sid@gmail.com 2026-01-23 23:25:19 +05:30
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environments/README.md
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@ -66,9 +66,9 @@ A flexible environment that integrates with the [Verifiers](https://docs.primein
**Output (Evaluation - `verifiers_eval.py`):**
Uses `evaluate_log()` from `atroposlib.envs.eval` to output:
Uses `evaluate_log()` from `BaseEnv` to output:
- Console: Metrics table with accuracy, avg_score, time, and per-reward function breakdown
- File: `metrics.json` and `samples.jsonl` (when `--eval-dir` is specified)
- File: `metrics.json` and `samples.jsonl` (when `--env.data_dir_to_save_evals` is specified)
**Configuration Options (`VfEnvConfig` for `verifiers_server.py`):**
@ -79,17 +79,19 @@ Uses `evaluate_log()` from `atroposlib.envs.eval` to output:
**CLI Options (`verifiers_eval.py`):**
Uses the standard BaseEnv CLI pattern with `evaluate` subcommand. Key options:
| Option | Type | Default | Description |
|--------|------|---------|-------------|
| `--server-url` | str | `http://localhost:8000/v1` | URL of the inference server |
| `--model-name` | str | (required) | Model name to evaluate |
| `--api-key` | str | `$OPENAI_API_KEY` | API key (defaults to env var) |
| `--vf-env-name` | str | `primeintellect/gsm8k` | Prime environment identifier |
| `--temperature` | float | `0.0` | Temperature for generation |
| `--max-tokens` | int | `2048` | Maximum tokens per completion |
| `--max-eval-items` | int | `-1` | Maximum items to evaluate (-1 for all) |
| `--max-concurrent` | int | `64` | Maximum concurrent requests |
| `--eval-dir` | str | `None` | Directory to save evaluation results |
| `--openai.base_url` | str | `http://localhost:9001/v1` | URL of the inference server |
| `--openai.model_name` | str | `Qwen/Qwen2.5-1.5B-Instruct` | Model name to evaluate |
| `--openai.api_key` | str | `x` | API key |
| `--env.vf_env_name` | str | `primeintellect/gsm8k` | Prime environment identifier |
| `--env.eval_temperature` | float | `0.0` | Temperature for generation |
| `--env.eval_max_tokens` | int | `2048` | Maximum tokens per completion |
| `--env.max_eval_items` | int | `-1` | Maximum items to evaluate (-1 for all) |
| `--env.max_concurrent` | int | `64` | Maximum concurrent requests |
| `--env.data_dir_to_save_evals` | str | `None` | Directory to save evaluation results |
**Usage Examples:**
@ -121,31 +123,33 @@ python verifiers_server.py evaluate \
--openai.base_url http://localhost:9001/v1
# Standalone Evaluation with OpenAI (verifiers_eval.py)
python eval_environments/verifiers_eval.py \
--server-url https://api.openai.com/v1 \
--model-name gpt-4o \
--vf-env-name primeintellect/gsm8k
python eval_environments/verifiers_eval.py evaluate \
--openai.base_url https://api.openai.com/v1 \
--openai.api_key $OPENAI_API_KEY \
--openai.model_name gpt-4o \
--env.vf_env_name primeintellect/gsm8k
# Quick test run with limited items
python eval_environments/verifiers_eval.py \
--server-url https://api.openai.com/v1 \
--model-name gpt-4o-mini \
--vf-env-name primeintellect/alphabet-sort \
--max-eval-items 10
python eval_environments/verifiers_eval.py evaluate \
--openai.base_url https://api.openai.com/v1 \
--openai.api_key $OPENAI_API_KEY \
--openai.model_name gpt-4o-mini \
--env.vf_env_name primeintellect/alphabet-sort \
--env.max_eval_items 10
# Evaluation with local server and results saved
python eval_environments/verifiers_eval.py \
--server-url http://localhost:9001/v1 \
--model-name Qwen/Qwen2.5-7B-Instruct \
--vf-env-name primeintellect/gsm8k \
--eval-dir ./eval_results
python eval_environments/verifiers_eval.py evaluate \
--openai.base_url http://localhost:9001/v1 \
--openai.model_name Qwen/Qwen2.5-7B-Instruct \
--env.vf_env_name primeintellect/gsm8k \
--env.data_dir_to_save_evals ./eval_results
```
**Key Implementation Details:**
- **RL Training Mode (`serve`)**: Uses `ManagedServer` for proper token/logprob alignment required by policy gradient methods (GRPO, PPO, REINFORCE). Returns `ScoredDataGroup` with `tokens`, `masks`, `scores`, and `inference_logprobs`.
- **SFT Datagen Mode (`process`)**: Uses `tokenize_for_trainer` to tokenize API responses with your target model's tokenizer (e.g., GPT-4o responses tokenized for Qwen/Llama). Does NOT require logprobs.
- **Evaluation (`verifiers_eval.py`)**: Standalone evaluation script using `EvalBase` pattern. Uses verifiers' native batch evaluation for efficiency and outputs results via `evaluate_log()`. Works with any OpenAI-compatible API.
- **Evaluation (`verifiers_eval.py`)**: Standalone evaluation script using `BaseEnv` pattern with `evaluate` subcommand. Uses verifiers' native batch evaluation for efficiency and outputs results via `evaluate_log()`. Works with any OpenAI-compatible API.
**Prime Environment Installation:**
```bash