Add NewtonBench Resource Server

[Kelvin0110]

Contributing To NeMo-Gym (NewtonBench Resource Server)

1) Basic information

i. Description of the environment

A resource server wrapping the NewtonBench benchmark

• Tasks: 324 scientific law discovery tasks across 12 physics domains.
• Observation Space: Experimental results (numeric or structured dictionaries) returned after tool use.
• Tools:
  • run_experiment: Query the environment with specific parameters to receive physical observations.
  • execute_python: (Optional) Python code-assisted discovery for complex data analysis.
• Server: FastAPI resource server following NeMo Gym conventions.

ii. Description of the verification logic

The verifier uses the NewtonBench evaluation suite to score the agent's proposed scientific law:

• Law Extraction: Attempts to find a law within <final_law> tags in the assistant's final response.
• Success Criteria: Evaluates both symbolic equivalence (via an LLM judge) and numeric accuracy (Root Mean Square Logarithmic Error - RMSLE).
• Reward Calculation:
  • reward = 0.3 * R_symbolic + 0.7 * R_numeric.
    • $R_{symbolic}$ is 1.0 if equivalent, -1.0 otherwise.
    • $R_{numeric} = 1.0 - (2.0 * \text{RMSLE} / (\text{RMSLE} + 3.0))$, yielding a score in $(-1, 1]$.
• /verify endpoint processes the agent's submission and returns these detailed performance metrics.

iii. Description of the prompts/tasks (source + domain)

Domain: Maths (Scientific Law Discovery).
Source: Tasks and prompts adapted from the NewtonBench benchmark, which instruct the agent to discover a specific shifted scientific law (e.g., Newton's Law of Gravitation, Snell's Law) by performing interactive experiments.

iv. License information

• Code: Apache 2.0.
• Data: Apache 2.0
• NewtonBench Benchmark: MIT (Copyright (c) 2025 HKUST-KnowComp).

2) Environment validity check

i. Commands used to collect rollouts

# Start NeMo Gym servers (agent + NewtonBench)
config_paths="resources_servers/newton_bench/configs/newton_bench.yaml,\
responses_api_models/vllm_model/configs/vllm_model.yaml"
ng_run "+config_paths=[$config_paths]"

# Collect sample rollouts
ng_collect_rollouts \
    +agent_name=newton_bench_simple_agent \
    +input_jsonl_fpath=resources_servers/newton_bench/data/example.jsonl \
    +output_jsonl_fpath=resources_servers/newton_bench/data/example_rollouts.jsonl \
    +limit=5

# View rollouts
ng_viewer +jsonl_fpath=resources_servers/newton_bench/data/example_rollouts.jsonl

ii. Resulting rollouts (5 examples)

See resources_servers/newton_bench/data/example_rollouts.jsonl
Expected behavior:

• Agent performs several experiments, analyzes data, and submits a scientific law.
• Successful discovery $\rightarrow$ positive reward ($\approx$ 1.0).
• Failed discovery $\rightarrow$ reward $\approx$ 0.0 or negative.

3) Tests

i. Commands used to run the tests

source resources_servers/newton_bench/.venv/bin/activate 
pytest resources_servers/newton_bench/tests/test_app.py

Coverage notes:
Resource server tests provide comprehensive coverage of the following areas:

• Session Lifecycle: Successful seeding, error handling for invalid modules, session ending, and background cleanup.
• Experiment Execution: Dynamic handler registration for each modules, basic run experiment execution, and error handling for uninitialized sessions, mismatched module calls, etc.
• Python Sandbox: Basic execution, session-based code persistence, timeout enforcement, and security validation (restricting dangerous imports/operations).
• Verification Logic: Law extraction from diverse response structures, and reward calculation via symbolic equivalence (LLM judge) and numeric RMSLE.

4) Reward profiling

Models: Qwen/Qwen3-VL-8B-Thinking

Method:

• 108 prompts based on version v0 of scientific laws.
• 4 rollouts per prompt (432 total).
• Tool calling of run_experiment enabled and agent loops until law submission.

Results:
Overall Metrics

• Total Rollouts: 432
• Mean Reward: $\approx$ 0.0675
• Median Reward: 0.0
• Min Reward: $\approx$ -0.8786
• Max Reward: 1.0

Tool Call Statistics

• Average Tool Calls: 22.95 per rollout
• Min Tool Calls: 0
• Max Tool Calls: 1770
• Correlation (tool calls $\leftrightarrow$ reward): $\approx$ -0.0211 (Weak negative correlation)

Reward Distribution (Buckets)

Reward Range	Count
[-1.0, -0.8)	16
[-0.8, -0.6)	16
[-0.6, -0.4)	60
[-0.4, -0.2)	39
[-0.2, 0.0)	24
[0.0, 0.2)	150
[0.2, 0.4)	46
[0.4, 0.6)	2
[0.6, 0.8)	1
[0.8, 1.0]	78

Performance by Tool Call Count Bins

Tool Call Range	Rollouts (n)	Mean Reward
0	23	$\approx$ -0.1112
1–10	329	$\approx$ 0.0824
11–50	60	$\approx$ 0.1308
51–200	15	$\approx$ -0.1959
201–2000	5	$\approx$ -0.0600

Key observations:

• Symbolic Accuracy: Approximately 19.7% symbolic accuracy and a widespread RMSLE distribution indicate frequent failures to recover exact symbolic forms or precise numeric behavior.
• Reward Distribution: Rewards cluster near zero (median 0.0, mean ~0.0675) with a long tail and many negative outcomes, reflecting frequent partial or failed discoveries.
• Tool Usage Sweet Spot: Positive performance is observed with moderate tool use (1–50 calls), with a peak in the 11–50 range, suggesting that tool-driven data collection is critical for inducing scientific laws.
• Diminishing Returns: Performance declines sharply after 50 calls, showing additional tool calls become detrimental and successful discovery depends on reasoning and hypothesis selection rather than raw data volume.

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[cmunley1]

can you please merge main?

[Kelvin0110]

Sure, I’ve merged the latest main branch. Please let me know if you’d like me to take any further steps.

[cmunley1]

have you tried training with NeMo RL (ideally we can test training before merging)? Also, I see you used a vision language model, does anything require vision here (not an issue, just curious) ?

[cmunley1]

DCO is faililng can you try to resolve that? https://docs.nvidia.com/nemo/gym/latest/contribute/development-setup.html#dco-and-commit-signing

Also see here https://docs.nvidia.com/nemo/gym/latest/contribute/environments/new-environment.html#contribution-workflow

[cmunley1]

please also run pre-commit check like ruff https://docs.nvidia.com/nemo/gym/latest/contribute/development-setup.html#pre-commit-hook-failures

[cmunley1]

need to pass dco and precommit

[Kelvin0110]

Thanks for checking.
Our resource server doesn’t require any vision. We selected Qwen/Qwen3-VL-8B-Thinking because this vision language model provides stronger pure text performance than the corresponding non‑VL models (e.g., qwen3-8b-thinking). Since our tasks involve relatively complex reasoning, using the stronger model helps ensure more stable and reliable reward distribution.

[newtdes]

Also for DCO and precommit, we will handle that to ensure our pull request will pass both checking