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nla-qwen2.5-7b-L20-v3-rl-checkpoints

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qwen2.5
checkpoints
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  16. iter_0000200/actor_dcp/optimizer/__1_0.distcp +3 -0
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  18. iter_0000200/actor_dcp/optimizer/__3_0.distcp +3 -0
  19. iter_0000200/actor_dcp/rng.pt +3 -0
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+ kind: nla_model
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+ schema_version: 2
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+ role: actor
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+ stage: rl
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+ base_checkpoint: /workspace/models/av_v3_ws
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+ d_model: 3584
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+ extraction:
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+ injection_scale: 150.0
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+ mse_scale: 59.86651818838306
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+ tokens:
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+ injection_char: "\u320E"
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+ injection_token_id: 149705
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+ injection_left_neighbor_id: 29
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+ injection_right_neighbor_id: 522
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+ critic_suffix_ids: null
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+ prompt_templates:
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+ actor: 'You are a meticulous AI researcher conducting an important investigation
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+ into activation vectors from a language model. Your overall task is to describe
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+ the semantic content of that activation vector.
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+
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+
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+ We will pass the vector enclosed in <concept> tags into your context. You must
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+ then describe that vector as a list of bullet points, one per line, ordered from
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+ the most to the least salient aspect.
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+
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+
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+ Here is the vector:
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+
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+ <concept>{injection_char}</concept>
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+
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+
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+ Please provide the bullet points.'
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+ critic: 'Summary of the following text: <text>{explanation}</text> <summary>'
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+ trained_on:
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+ - /workspace/out/rl_v3.parquet
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+ parent_checkpoints:
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+ - /workspace/models/av_v3_ws
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+ created_at: '2026-07-02T02:55:40.140603+00:00'
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+ created_by: nla.train_actor.NLAFSDPActor
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+ training:
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+ rollout_id: 199
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+ lr: 1.0e-05
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+ loss_type: policy_loss
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+ global_batch_size: 512
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