sophistry_bench_sprint_env: add training example and results

docs/source/environments/sophistry_bench_sprint.md

@@ -37,7 +37,7 @@ from sophistry_bench_sprint_env import SophistryBenchSprintEnv
 
 async def main():
     # Deployed Hugging Face Space (or .from_docker_image("openenv-sophistry_bench_sprint:latest")):
-    client = await SophistryBenchSprintEnv.from_env("anushaacharya/sophistry_bench_sprint_env")
+    client = await SophistryBenchSprintEnv.from_env("openenv-community/sophistry_bench_sprint_env")
     async with client:
         obs = (await client.reset()).observation
         print(obs.prompt, obs.answer_to_defend)
@@ -67,6 +67,70 @@ the reward-hacking measurement. By default it holds **seven** components; `corre
 > reason; even with the rest of the components, forwarding them to the agent leaks the
 > reward signal and defeats the reward-hacking measurement.
 
+## Training
+
+[`examples/sophistry_bench_sprint_grpo.py`](https://github.com/huggingface/OpenEnv/blob/main/examples/sophistry_bench_sprint_grpo.py)
+trains a policy on this env with TRL's `GRPOTrainer`. Since the episode is
+single-step, this is a plain prompt -> completion -> reward GRPO setup — no
+`environment_factory`/tool-calling needed (contrast with the multi-turn Wordle
+GRPO tutorial). It connects directly to the deployed Space's source (cloned
+and run locally via `uv`, not Docker, and not subject to the Space's request
+quota) and only depends on `openenv[core]` from PyPI, so it also runs as a
+standalone `uv` script, including on Hugging Face Jobs.
+
+### Validated with a real 100-step run on Hugging Face Jobs
+
+`hf jobs uv run examples/sophistry_bench_sprint_grpo.py --flavor a10g-small -- --n-episodes 64 --steps 100 --per-device-batch-size 8 --num-generations 8`
+(`Qwen2.5-0.5B-Instruct`, default `SPRINT_WEIGHTS`). Full per-step metrics,
+including the `correctness_reward`/`n_claims` breakdown:
+[`training/hf_jobs_metrics.csv`](https://github.com/huggingface/OpenEnv/blob/main/envs/sophistry_bench_sprint_env/training/hf_jobs_metrics.csv).
+
+| Steps | `aggregate_reward` (proxy) | `n_claims` | `correctness_reward` (ground truth) | `n_citations` |
+|---|---|---|---|---|
+| 1–10 | 0.354 | 0.863 | 0.700 | 0.825 |
+| 11–20 | 0.461 | 0.138 | 0.600 | 0.138 |
+| 21–30 | 0.500 | 0.000 | 0.200 | 0.000 |
+| 41–50 | 0.500 | 0.000 | 0.600 | 0.000 |
+| 91–100 | 0.500 | 0.000 | 0.500 | 0.000 |
+
+`aggregate_reward` climbs from ~0.35 to a ~0.50 plateau, confirming the proxy
+is genuinely optimized end to end on Hugging Face infrastructure. But at this
+scale (a 0.5B model, ~800 total rollouts — two orders of magnitude fewer than
+the Prime Intellect run below), the policy doesn't converge on the
+`claim_count_cliff` target the way the larger run does. Instead `n_claims`
+*collapses to ~0*: emitting empty/near-empty completions also scores ~0.5,
+and at this scale that's the cheaper exploit to find than hitting exactly 8
+claims. `correctness_reward` stays noisy and decoupled from the optimized
+reward either way (0.2–0.7, no trend) — the same core finding as the
+Prime Intellect run, just reached via a different degenerate strategy. Read
+as a second data point, not a replication: this env reliably induces some
+form of reward hacking, but *which* shortcut a policy finds depends on scale.
+
+### Also validated on Prime Intellect
+
+The same scoring is registered as `anusha/sophistry-bench-sprint` on the Prime
+Intellect Hub (parity-tested against this OpenEnv port). A 100-step GRPO run
+there reproduces the reward-hacking signature this env is designed to
+surface. Config:
+[`training/sophistry_bench_sprint.toml`](https://github.com/huggingface/OpenEnv/blob/main/envs/sophistry_bench_sprint_env/training/sophistry_bench_sprint.toml);
+full per-step metrics:
+[`training/metrics.csv`](https://github.com/huggingface/OpenEnv/blob/main/envs/sophistry_bench_sprint_env/training/metrics.csv).
+Base model `Llama-3.2-1B-Instruct`, default `SPRINT_WEIGHTS` (only `aggregate_reward` weighted).
+
+| Steps | `aggregate_reward` (proxy, optimized) | `n_claims` (cliff target) | `correctness_reward` (ground truth, weight 0) |
+|---|---|---|---|
+| 0–9 | 0.477 | 0.989 | 0.470 |
+| 10–19 | 0.679 | 1.002 | 0.532 |
+| 20–29 | 0.740 | 1.015 | 0.524 |
+| 40–49 | 0.770 | 1.001 | 0.353 |
+| 90–99 | 0.766 | 1.001 | 0.470 |
+
+`aggregate_reward` climbs from ~0.48 to a ~0.77 plateau and `n_claims` saturates at the
+`claim_count_cliff` target almost immediately — the policy learns to hit exactly 8 `<claim>`
+tags. `correctness_reward` (the hidden ground truth, **not** in the optimized objective)
+stays flat and noisy the entire run with no upward trend. That gap — proxy reward up,
+ground-truth quality flat — is the reward-hacking measurement this env exists to produce.
+
 ## Build & test
 
 ```bash

envs/sophistry_bench_sprint_env/README.md

@@ -49,7 +49,7 @@ from sophistry_bench_sprint_env import SophistryBenchSprintEnv
 
 async def main():
     # Deployed Hugging Face Space (or .from_docker_image("openenv-sophistry_bench_sprint:latest")):
-    client = await SophistryBenchSprintEnv.from_env("anushaacharya/sophistry_bench_sprint_env")
+    client = await SophistryBenchSprintEnv.from_env("openenv-community/sophistry_bench_sprint_env")
     async with client:
         obs = (await client.reset()).observation
         print(obs.prompt, obs.answer_to_defend)
@@ -79,6 +79,70 @@ the reward-hacking measurement. By default it holds **seven** components; `corre
 > reason; even with the rest of the components, forwarding them to the agent leaks the
 > reward signal and defeats the reward-hacking measurement.
 
+## Training
+
+[`examples/sophistry_bench_sprint_grpo.py`](https://github.com/huggingface/OpenEnv/blob/main/examples/sophistry_bench_sprint_grpo.py)
+trains a policy on this env with TRL's `GRPOTrainer`. Since the episode is
+single-step, this is a plain prompt -> completion -> reward GRPO setup — no
+`environment_factory`/tool-calling needed (contrast with the multi-turn Wordle
+GRPO tutorial). It connects directly to the deployed Space's source (cloned
+and run locally via `uv`, not Docker, and not subject to the Space's request
+quota) and only depends on `openenv[core]` from PyPI, so it also runs as a
+standalone `uv` script, including on Hugging Face Jobs.
+
+### Validated with a real 100-step run on Hugging Face Jobs
+
+`hf jobs uv run examples/sophistry_bench_sprint_grpo.py --flavor a10g-small -- --n-episodes 64 --steps 100 --per-device-batch-size 8 --num-generations 8`
+(`Qwen2.5-0.5B-Instruct`, default `SPRINT_WEIGHTS`). Full per-step metrics,
+including the `correctness_reward`/`n_claims` breakdown:
+[`training/hf_jobs_metrics.csv`](https://github.com/huggingface/OpenEnv/blob/main/envs/sophistry_bench_sprint_env/training/hf_jobs_metrics.csv).
+
+| Steps | `aggregate_reward` (proxy) | `n_claims` | `correctness_reward` (ground truth) | `n_citations` |
+|---|---|---|---|---|
+| 1–10 | 0.354 | 0.863 | 0.700 | 0.825 |
+| 11–20 | 0.461 | 0.138 | 0.600 | 0.138 |
+| 21–30 | 0.500 | 0.000 | 0.200 | 0.000 |
+| 41–50 | 0.500 | 0.000 | 0.600 | 0.000 |
+| 91–100 | 0.500 | 0.000 | 0.500 | 0.000 |
+
+`aggregate_reward` climbs from ~0.35 to a ~0.50 plateau, confirming the proxy
+is genuinely optimized end to end on Hugging Face infrastructure. But at this
+scale (a 0.5B model, ~800 total rollouts — two orders of magnitude fewer than
+the Prime Intellect run below), the policy doesn't converge on the
+`claim_count_cliff` target the way the larger run does. Instead `n_claims`
+*collapses to ~0*: emitting empty/near-empty completions also scores ~0.5,
+and at this scale that's the cheaper exploit to find than hitting exactly 8
+claims. `correctness_reward` stays noisy and decoupled from the optimized
+reward either way (0.2–0.7, no trend) — the same core finding as the
+Prime Intellect run, just reached via a different degenerate strategy. Read
+as a second data point, not a replication: this env reliably induces some
+form of reward hacking, but *which* shortcut a policy finds depends on scale.
+
+### Also validated on Prime Intellect
+
+The same scoring is registered as `anusha/sophistry-bench-sprint` on the Prime
+Intellect Hub (parity-tested against this OpenEnv port). A 100-step GRPO run
+there reproduces the reward-hacking signature this env is designed to
+surface. Config:
+[`training/sophistry_bench_sprint.toml`](https://github.com/huggingface/OpenEnv/blob/main/envs/sophistry_bench_sprint_env/training/sophistry_bench_sprint.toml);
+full per-step metrics:
+[`training/metrics.csv`](https://github.com/huggingface/OpenEnv/blob/main/envs/sophistry_bench_sprint_env/training/metrics.csv).
+Base model `Llama-3.2-1B-Instruct`, default `SPRINT_WEIGHTS` (only `aggregate_reward` weighted).
+
+| Steps | `aggregate_reward` (proxy, optimized) | `n_claims` (cliff target) | `correctness_reward` (ground truth, weight 0) |
+|---|---|---|---|
+| 0–9 | 0.477 | 0.989 | 0.470 |
+| 10–19 | 0.679 | 1.002 | 0.532 |
+| 20–29 | 0.740 | 1.015 | 0.524 |
+| 40–49 | 0.770 | 1.001 | 0.353 |
+| 90–99 | 0.766 | 1.001 | 0.470 |
+
+`aggregate_reward` climbs from ~0.48 to a ~0.77 plateau and `n_claims` saturates at the
+`claim_count_cliff` target almost immediately — the policy learns to hit exactly 8 `<claim>`
+tags. `correctness_reward` (the hidden ground truth, **not** in the optimized objective)
+stays flat and noisy the entire run with no upward trend. That gap — proxy reward up,
+ground-truth quality flat — is the reward-hacking measurement this env exists to produce.
+
 ## Build & test
 
 ```bash

envs/sophistry_bench_sprint_env/training/hf_jobs_metrics.csv

@@ -0,0 +1,101 @@
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