`miniwob.find-greatest` can be marked successful after selecting a non-maximum card
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- javascript, python
- Domain
- ai, testing-qa
Research direction
Start with the attached source/browsergym_miniwob_base.py and trace how RAW_REWARD_GLOBAL becomes the reported reward and success value; compare that with the wrong-card branch in source/find-greatest.html. Check the linked pull request #396 and any relevant tests before choosing a fix. Done means submitting a non-maximum card no longer reports success, while selecting the greatest card still does.
Written by the indexing model from the issue text.
Description
Summary
There appears to be a success-threshold bug in the BrowserGym MiniWoB wrapper for miniwob.find-greatest.
The task text says:
Find and pick the card with the greatest number, then press submit.
However, a run can be marked successful after selecting a card that is not the greatest card.
In the attached concrete run, the page had cards with values 4, 7, and 2. The agent selected the card with value 2 and submitted. The final DOM still shows another card with value 7, so this should not satisfy the task. The native evaluator nevertheless reports success: true.
Why this happens
In MiniWoB++ find-greatest.html, selecting the correct card ends with reward 1.0, but selecting the wrong card and submitting still ends the episode with a positive partial reward:
if(userIndex === expectedIndex.toString()) core.endEpisode(1.0, true);
else core.endEpisode(0.1, true);
In BrowserGym's MiniWoB wrapper, AbstractMiniwobTask.validate() converts any positive raw reward into a full success reward:
reward = float(info["RAW_REWARD_GLOBAL"] > 0) # TODO: shouldn't it be 0.5?
As a result, the wrong-card partial reward 0.1 becomes reward: 1.0 and success: true in the run output.
Actual behavior in the attached run
The attached evidence/native_evaluator_output_agent_a.json shows:
"RAW_REWARD_GLOBAL": 0.1,
"reward": 1.0,
"success": true
It also shows the agent actions:
"action": "click('17')"
"action": "click('20')"
The attached final DOM snapshot evidence/final_dom_step_002_agent_a.html shows that bid 17 is the revealed selected card, with value 2:
<div class="card" data-index="2" ... bid="17">
<span class="card-value" ...>2</span>
</div>
The same DOM line also shows another card with value 7:
<div class="card hidden" data-index="1" ... bid="15">
<span class="card-value" ...>7</span>
</div>
So the submitted card was not the greatest card, but the run was still reported as successful.
Expected behavior
For find-greatest, BrowserGym should only report success when the selected card is actually the greatest card.
Possible fixes:
- change the MiniWoB success threshold so partial positive rewards such as
0.1do not count as success, for exampleRAW_REWARD_GLOBAL > 0.5orRAW_REWARD_GLOBAL >= 1.0; or - add task-specific handling where partial rewards are not treated as benchmark success; or
- if the intended fix belongs in MiniWoB++, make wrong-card submission produce a non-success raw reward rather than
0.1.
The issue seems most directly triggered by BrowserGym treating any RAW_REWARD_GLOBAL > 0 as success.
Attached evidence package
source/browsergym_miniwob_base.py: BrowserGym MiniWoB wrapper showingreward = float(info["RAW_REWARD_GLOBAL"] > 0).source/find-greatest.html: MiniWoB++ task source showing the task text and wrong-cardcore.endEpisode(0.1, true)branch.evidence/native_evaluator_output_agent_a.json: run output showingRAW_REWARD_GLOBAL=0.1,reward=1.0, andsuccess=true.evidence/final_dom_step_002_agent_a.html: final DOM showing selected card value2while another card value is7.evidence/final_screenshot_step_002_agent_a.png: final screenshot for the same run.evidence/validation_final_agent_a.jsonandevidence/task_info_final_agent_a.json: final validator/task state.evidence/miniwob_scores_flat.csv: score table; row 170 contains thisminiwob.find-greatest / Agent Arun.
- Dominant language
- Python
- Stars
- 1.4k
- Forks
- 202
- Avg merge
- 6d 15h
- Merged PRs (30d)
- 3
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