[Question] About 2048 scoring

I try 2048 example and only one scores in the end (step 27), other is always {'scores': []}

[RULER] Pretty-printed LLM choice JSON:
{'scores': []}
 Swallowed exception: 
Skipping tuning as there is no suitable data. This can happen when all the trajectories in the same group have the same reward and thus no advantage to train on.
Advanced step from 25 to 26 (no training occurred)
gather: 100%|███████████| 9/9 [00:34<00:00,  3.80s/it, reward=1.17, invalid_move=0.111, max_value=71.3, board_value=159, num_moves=71, win=0.333, prompt_tokens=2.04e+3, completion_tokens=21.3, total_completion_tokens=5240.0]

[RULER] Pretty-printed LLM choice JSON:
{'scores': []}
 Swallowed exception: 
Skipping tuning as there is no suitable data. This can happen when all the trajectories in the same group have the same reward and thus no advantage to train on.
Advanced step from 26 to 27 (no training occurred)
gather: 100%|█████████| 9/9 [00:39<00:00,  4.40s/it, reward=1.49, invalid_move=0.111, max_value=93.6, board_value=162, num_moves=72.3, win=0.667, prompt_tokens=2.21e+3, completion_tokens=21.3, total_completion_tokens=5336.0]

[RULER] Pretty-printed LLM choice JSON:
{
    'scores': [
        {
            'trajectory_id': '1',
            'explanation': "This trajectory starts with a very basic board state (2, 2 at top) and makes only one move 'up'. It doesn't achieve the goal of reaching 2048, but shows early progress toward organizing cells. It 
gets partial credit for making a move that could potentially lead to combining cells.",
            'score': 0.1
        },
        {
            'trajectory_id': '2',
            'explanation': 'This trajectory demonstrates excellent 2048 gameplay, reaching high values including a 32 and continuing to build up the board efficiently. It makes strategic moves without getting stuck and 
progresses toward 2048 successfully.',
            'score': 0.95
        },
        {
            'trajectory_id': '3',
            'explanation': 'This trajectory also performs very well, reaching high tile values and successfully building up the board. It shows strong strategic play with consistent progress toward 2048.',
            'score': 0.9
        },
        {
            'trajectory_id': '4',
            'explanation': 'This trajectory shows good 2048 playing, reaching tile values up to 32. It makes strategic moves but has some inefficient patterns that could be improved, though still performs well overall.',
            'score': 0.8
        },
        {
            'trajectory_id': '5',
            'explanation': 'This trajectory reaches tile values up to 32. It is very strategic and shows good board organization, but has some inefficient moves compared to trajectory 2 or 3.',
            'score': 0.85
        },
        {
            'trajectory_id': '6',
            'explanation': 'This trajectory also shows good 2048 playing, reaching tiles up to 32. While it progresses well and shows planning ahead, it has some suboptimal moves that make it less efficient than the top 
performers.',
            'score': 0.8
        },
        {
            'trajectory_id': '7',
            'explanation': 'This trajectory makes good progress toward 2048, reaching high tile values. However, its movements are less consistently efficient compared to top trajectories in terms of combining cells 
effectively.',
            'score': 0.75
        },
        {
            'trajectory_id': '8',
            'explanation': "This trajectory reaches high tiles but shows some inefficiencies in its approach. It makes moves that don't always leverage existing cell combinations effectively.",
            'score': 0.7
        },
        {
            'trajectory_id': '9',
            'explanation': 'This trajectory shows moderate progress, reaching some high tiles but with several inefficient moves that prevent it from performing as well as the top trajectories.',
            'score': 0.65
        }
    ]
}

Only the change I made is I use local qwen3-30b-a3b-thinking-2507 and SIMULTANEOUS_GAMES = 9.
Just curious btw. 😗

Start with examples/2048 and reproduce the run using qwen3-30b-a3b-thinking-2507 and SIMULTANEOUS_GAMES = 9. Inspect the swallowed exception and the cases producing {'scores': []}, then determine whether the behavior is expected or identify a reproducible fix; done means the cause and expected scoring behavior are established.