On-Policy Corrections

1. [First turn] User message
2. Assistant Reasoning message
3. Assistant Chat
4. Assistant Tool Call
5. Tool response
6. Reasoning
7. Chat
8. Tool Call
9. Tool
10. [First turn, third step] Chat
11. [Second turn] User message
12. …

Abbreviated notation: U R C TC T R C TC T C U

• U: User message (independent of model)
• T: Tool response (independent of model)
• R, C, TC: Reasoning, Chat, Tool Call (from model endpoint)

Most model endpoints return [R C TC] messages in a single response, so the rollout can be viewed as: U [R C TC] T [R C TC] T [C] U, where brackets indicate a single model call.

In the previous model call, the model may produce token IDs 1 and 2 which de-tokenize to _Skinny in LF5. Then in LF3 of the next call, _Skinny might re-tokenize to token ID 3.

At generation time, logprobs for tokens following _Skinny are calculated using token IDs 1 and 2. At train time, the same logprobs are calculated using token ID 3, creating a mismatch.

Observed scenarios:

1. Merging: Token IDs 1 and 2 re-tokenize to single token ID 3
   • Example: "_Ski" + "nny" → "_Skinny"
2. Different split: Token IDs 1 and 2 re-tokenize to different token IDs 3 and 4
   • Example: "_Ski" + "nny" → "_Skin" + "ny"

At LF6, the model may produce token IDs that de-tokenize to:

1
<tool_call><name>get_weather</name><parameters>{"city": "SF"}</parameters></tool_call>

This converts to an OpenAI tool call object:

1
{"type": "function", "function": "get_weather", "arguments": "{\"city\": \"SF\"}"}

At LF2 in the next call, the chat template may render this differently:

1
<tool_call>
2
<name>
3
get_weather
4
</name>
5
<parameters>
6
{"city": "SF"}
7
</parameters>
8
</tool_call>

The deterministic chat template cannot match the stochastic model output format exactly.

Developers sometimes modify rollout history:

1. Agentic coding harnesses: Summarize or truncate prior history as rollouts grow longer
2. Model chat templates: Remove reasoning from input prompt across turns

These changes alter the prompt token IDs the model sees at the current call, differing from the final prompt token IDs used for training.

Prerequisites:

• model_prefix_token_ids: Ground truth prompt token IDs concatenated with generation token IDs from the previous model call
• template_prefix_token_ids: Re-templated and re-tokenized token IDs up to (not including) the final assistant message
• template_token_ids: Re-templated and re-tokenized token IDs for the entire rollout

Assumption: template_prefix_token_ids is a strict prefix of template_token_ids (requires circumventing Problem 3).

Algorithm:

The fix finds the position of the correct EOS token ID in template_token_ids and splices in model_prefix_token_ids.

Example:

1. Current request (LF1) contains rollout structure: U A T A U A T
2. Variables:
   • model_prefix_token_ids: Ground truth token IDs for U A T A U A
   • template_prefix_token_ids: Re-templated token IDs for U A T A U A
   • template_token_ids: Re-templated token IDs for U A T A U A T
3. Use template_prefix_token_ids to find the EOS token position corresponding to model_prefix_token_ids
4. Splice template_token_ids prefix with model_prefix_token_ids