The model learns not to guess the final answer, but to plan and verify each step of reasoning.
🟢 How it works?
- Expert solutions are cut into small steps : The model learns to think step-by-step, not just copy the solution
- SRL gives a reward for each step in the chain so model takes a step → receives a score of closeness to the expert
- Small models receive a real training signal and also start planning
- Uses text-matcher + a small format penalty
- Updates in GRPO style with dynamic batch selection to avoid empty signals
The model gains Early planning, Correction on the go, Self-checking of the result
- Also answers don't get longer - Quality grows due to thinking, not rambling
SRL looks like a natural bridge between supervised training and classic RL: controlled stability + depth of reasoning.
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