Thanks for checking this out! A few additional details that didn't fit in the main post:
The system maintains two separate limits: a storage limit (max 10 strategies per problem type in the database) and an inference limit (max 3 strategies applied per query). This keeps the database manageable while ensuring the system prompt doesn't get too long.
One interesting finding was that strategies only get used for inference once they have at least 5 attempts and a 40% success rate. This prevents the system from applying unproven strategies to new problems.
The approach works particularly well with reasoning models like DeepSeek-R1 and QwQ - the learned strategies seem to guide their thinking process effectively.
I'm especially curious about:
1. How this might work with different model families
2. Whether the community sees value in sharing strategy databases between users
3. Ideas for extending beyond text-based reasoning to multimodal problems
The plugin integrates with our broader optillm project which has other inference optimization techniques. You can combine SPL with methods like mixture-of-agents or MCTS using the "&" operator.
Next I'm thinking about meta-learning - having the system learn how to create better strategies more efficiently. Also exploring collaborative strategy sharing.
Would love to hear thoughts on the approach or if anyone has ideas for other problem domains where this might be useful!
That's an interesting space to explore! I'm wondering about the baseline in the benchmarks. Which prompts did you use for those? I'm asking because some of the resulting prompts seem fairly generic, and I'm wondering if you could just blanket add them to each prompt and also see an improvement. Things like "Identify the question (what are you trying to find?)".
In the same vein, wouldn't it be interesting to measure which part of the prompt most contributed to better solving the problem? Surely some parts will be just noise and can be trimmed away.
Also wondering what this does, since the model probably won't (can't?) actually read the problem multiple times:
Here the system seems to have discovered this strategy by itself. The prompts are generic because during learning there is a part to refine and combine them. I haven’t experimented yet by adding all prompts to every query, given the large context it will be interesting to see.
Okay, but it looks like in the paper, they are actually adding the question twice in the prompt, not just instructing the model to read it twice. Or am I missing something?
This is a really cool idea! I recently came across another project on GitHub: https://github.com/tensorzero/tensorzero that explores a similar direction. You might find it interesting, and perhaps it could offer some inspiration or useful insights for your work as well.
If I jump in and, say, manually 'tweak' one of those JSON strategies because I think I have a better idea, what happens next? Does the LLM just roll with my brilliant human intervention, or could it eventually 'learn' that my tweak was actually counterproductive and refine it back (or away from my edit)?
You can run in two modes, by default you run in the inference mode without learning. So, the changes you made will be used. If you switch to learning mode then the strategies are updated/refined and merged based on a config that you can control.
# How often to perform maintenance operations (merge, prune)
MAINTENANCE_INTERVAL = 40
# Strategy selection thresholds
STRATEGY_CREATION_THRESHOLD = 0.7 # Higher threshold to avoid creating similar strategies
STRATEGY_MERGING_THRESHOLD = 0.6 # Lower threshold to merge more similar strategies
MIN_SUCCESS_RATE_FOR_INFERENCE = 0.4 # Minimum success rate for a strategy to be used during inference
You should take a look at something called Case-based reasoning. Seems to perfectly fit into the road you are currently walking, as you basically just rediscovered the CBR-cycle.
We do not allow the strategies to keep growing there is a refinement phase where we refine and merge existing strategies. The experiments were run with this config - https://github.com/codelion/optillm/blob/main/optillm/plugin... which allows a maximum of 10 strategies of each type.
E.g. This was the strategy discovered by optiLLM for solving word problems:
*Refined Strategy for Solving Word Problems:*
1. *Understand:*\n * Read the problem carefully (multiple times).\n * Identify the question (what are you trying to find?).\n * List all given information (facts, numbers, units).\n * Clarify ambiguous terms/units.
2. *Organize Information & Identify Unknowns:*\n * Choose an organization method: (e.g., table, diagram, list, drawing).\n * Clearly identify the unknowns (what you need to solve for).
3. *Plan and Translate:*\n * Define all variables with units (e.g., `p = number of pennies`, `c = number of compartments`).\n * Identify relationships between knowns and unknowns.\n * Convert units if necessary.\n * Write equations or expressions, including units, that relate the knowns and unknowns.\n * Ensure units are consistent throughout the equations.\n * Outline the solution steps.
4. *Solve:*\n * Show work step-by-step.\n * Track units throughout calculations.\n * Calculate accurately.\n * Solve for the unknowns.\
5. *Evaluate and Verify:*\n * Check if the answer is reasonable.\n * Verify the answer.
Thanks for checking this out! A few additional details that didn't fit in the main post:
The system maintains two separate limits: a storage limit (max 10 strategies per problem type in the database) and an inference limit (max 3 strategies applied per query). This keeps the database manageable while ensuring the system prompt doesn't get too long.
One interesting finding was that strategies only get used for inference once they have at least 5 attempts and a 40% success rate. This prevents the system from applying unproven strategies to new problems.
The approach works particularly well with reasoning models like DeepSeek-R1 and QwQ - the learned strategies seem to guide their thinking process effectively.
I'm especially curious about:
1. How this might work with different model families
2. Whether the community sees value in sharing strategy databases between users
3. Ideas for extending beyond text-based reasoning to multimodal problems
The plugin integrates with our broader optillm project which has other inference optimization techniques. You can combine SPL with methods like mixture-of-agents or MCTS using the "&" operator.
Next I'm thinking about meta-learning - having the system learn how to create better strategies more efficiently. Also exploring collaborative strategy sharing.
Would love to hear thoughts on the approach or if anyone has ideas for other problem domains where this might be useful!
That's an interesting space to explore! I'm wondering about the baseline in the benchmarks. Which prompts did you use for those? I'm asking because some of the resulting prompts seem fairly generic, and I'm wondering if you could just blanket add them to each prompt and also see an improvement. Things like "Identify the question (what are you trying to find?)".
In the same vein, wouldn't it be interesting to measure which part of the prompt most contributed to better solving the problem? Surely some parts will be just noise and can be trimmed away.
Also wondering what this does, since the model probably won't (can't?) actually read the problem multiple times:
Re-reading the problem apparently works well - https://arxiv.org/abs/2309.06275
Here the system seems to have discovered this strategy by itself. The prompts are generic because during learning there is a part to refine and combine them. I haven’t experimented yet by adding all prompts to every query, given the large context it will be interesting to see.
Okay, but it looks like in the paper, they are actually adding the question twice in the prompt, not just instructing the model to read it twice. Or am I missing something?
This is a really cool idea! I recently came across another project on GitHub: https://github.com/tensorzero/tensorzero that explores a similar direction. You might find it interesting, and perhaps it could offer some inspiration or useful insights for your work as well.
If I jump in and, say, manually 'tweak' one of those JSON strategies because I think I have a better idea, what happens next? Does the LLM just roll with my brilliant human intervention, or could it eventually 'learn' that my tweak was actually counterproductive and refine it back (or away from my edit)?
You can run in two modes, by default you run in the inference mode without learning. So, the changes you made will be used. If you switch to learning mode then the strategies are updated/refined and merged based on a config that you can control.
# How often to perform maintenance operations (merge, prune)
MAINTENANCE_INTERVAL = 40
# Strategy selection thresholds
STRATEGY_CREATION_THRESHOLD = 0.7 # Higher threshold to avoid creating similar strategies
STRATEGY_MERGING_THRESHOLD = 0.6 # Lower threshold to merge more similar strategies
MIN_SUCCESS_RATE_FOR_INFERENCE = 0.4 # Minimum success rate for a strategy to be used during inference
The configs are all defined here - https://github.com/codelion/optillm/blob/main/optillm/plugin...
You should take a look at something called Case-based reasoning. Seems to perfectly fit into the road you are currently walking, as you basically just rediscovered the CBR-cycle.
How do you forsee a system like this efficiently managing and relying on a set of strategies whose size can become unbounded?
We do not allow the strategies to keep growing there is a refinement phase where we refine and merge existing strategies. The experiments were run with this config - https://github.com/codelion/optillm/blob/main/optillm/plugin... which allows a maximum of 10 strategies of each type.
I would like to see some interesting input/output pairs. Do you have any?
We have some examples in the plugin README: https://github.com/codelion/optillm/tree/main/optillm/plugin...
E.g. This was the strategy discovered by optiLLM for solving word problems:
*Refined Strategy for Solving Word Problems:*
1. *Understand:*\n * Read the problem carefully (multiple times).\n * Identify the question (what are you trying to find?).\n * List all given information (facts, numbers, units).\n * Clarify ambiguous terms/units.
2. *Organize Information & Identify Unknowns:*\n * Choose an organization method: (e.g., table, diagram, list, drawing).\n * Clearly identify the unknowns (what you need to solve for).
3. *Plan and Translate:*\n * Define all variables with units (e.g., `p = number of pennies`, `c = number of compartments`).\n * Identify relationships between knowns and unknowns.\n * Convert units if necessary.\n * Write equations or expressions, including units, that relate the knowns and unknowns.\n * Ensure units are consistent throughout the equations.\n * Outline the solution steps.
4. *Solve:*\n * Show work step-by-step.\n * Track units throughout calculations.\n * Calculate accurately.\n * Solve for the unknowns.\
5. *Evaluate and Verify:*\n * Check if the answer is reasonable.\n * Verify the answer.
6. *Summarize:*\n * State the answer with units
Full list of strategies discovered is available here -https://github.com/codelion/optillm/blob/main/optillm/plugin...